turnstone/docs/tools.md

Tools Reference

turnstone exposes 16 built-in tools plus any number of external MCP tools to the LLM via the OpenAI function-calling interface. Built-in tools are defined as JSON files under turnstone/tools/ and loaded at startup by turnstone/core/tools.py. MCP tools are discovered from configured MCP servers at startup by turnstone/core/mcp_client.py.

---

Tool Schema Format

Each JSON file in turnstone/tools/ contains a standard OpenAI function-calling schema plus turnstone-specific metadata keys:

{
  "name": "tool_name",
  "description": "What the tool does.",
  "parameters": {
    "type": "object",
    "properties": { ... },
    "required": ["param1"]
  },
  "task_agent": true,
  "auto_approve": true,
  "primary_key": "param1"
}

Metadata keys (stripped before sending the schema to the model):

Key	Type	Meaning
task_agent	bool	Tool is available to task sub-agents.
auto_approve	bool	Tool runs without user confirmation (read-only, safe operations).
primary_key	str	When the model sends a bare string instead of JSON args, map it to this parameter name.

---

Derived Tool Sets

turnstone/core/tools.py loads all JSON files and derives these collections:

Name	Description
TOOLS	All 28 loaded built-in tool definitions (interactive + coordinator union). Sessions send a kind-specific subset (INTERACTIVE_TOOLS or COORDINATOR_TOOLS).
TASK_AGENT_TOOLS	Tools with task_agent: true -- available to task sub-agents. Includes write operations.
TASK_AUTO_TOOLS	Set of all tool names with auto_approve: true -- used by task-agent sub-sessions to skip confirmation for matching available tools.
BUILTIN_TOOL_NAMES	Frozenset of all 28 built-in tool names (interactive + coordinator union). Used by tool search to distinguish always-on tools from deferrable MCP tools.
PRIMARY_KEY_MAP	Dict mapping tool name to its primary_key parameter name.

---

Execution Pipeline

See also: Tool Pipeline diagram

Tool execution follows a three-phase pipeline inside ChatSession._execute_tools():

Phase 1: Prepare

_prepare_tool(tc) is called for each tool call returned by the model.

• Parses the JSON arguments (with fallback for malformed JSON).
• If JSON parsing fails entirely, uses PRIMARY_KEY_MAP to map a bare string to the correct parameter.
• Dispatches to the matching _prepare_{func_name}() handler. There are 16 built-in tools plus tool_search (synthetic, client-side BM25 fallback) and the generic _prepare_mcp_tool() handler for MCP tools.
• Validates arguments and builds a preview dict containing:
  • call_id, func_name, header, preview (for display)
  • needs_approval (bool)
  • execute (callable to run the tool)
  • error (set if validation fails; tool will not execute)

Phase 2: Approve

All prepared items are sent to the UI via ui.approve_tools(items).

• The UI displays each tool's header and preview to the user.
• Items where needs_approval is False (auto-approved tools) are shown but do not block execution.
• Items where needs_approval is True require the user to accept or deny.
• The user can provide feedback alongside their approval (e.g. "y, use full path").
• Choosing "always" (key a) adds the pending tool names to auto_approve_tools, so that specific tool type is auto-approved going forward (other tool types still prompt). This is per-tool, not blanket.
• If auto_approve is True on the session (via --skip-permissions or workstream template), all tools are approved automatically.

Phase 3: Execute

Each item's execute callable is invoked:

• Single tool calls run directly on the current thread.
• Multiple tool calls run in parallel via ThreadPoolExecutor(max_workers=4).
• Errored or denied items return their error/denial message without executing.
• The bash tool streams stdout incrementally: each line calls ui.on_tool_output_chunk(call_id, line) as it is produced, then the final combined output (stdout + stderr) is delivered via ui.on_tool_result(call_id, name, output, is_error=...). The call_id links tool_info/approve_request items to their streaming chunks and final result, enabling correct routing when multiple bash tools run in parallel. The is_error flag is True when the tool execution failed (e.g. bash exit code >= 2 or signal, file not found, timeout). Exit code 1 is ambiguous and not flagged; user denials are tracked separately. This removes the need for text-prefix heuristics. Other tools deliver results atomically via ui.on_tool_result(call_id, name, output, is_error=...) only.

---

Tool Approval Flow

Auto-approved (no user confirmation needed at runtime):

• read_file -- reads files, no side effects
• search -- grep-style search, no side effects
• memory -- structured persistent memory (save/search/delete/list)
• recall -- searches conversation history
• notify -- sends notifications to linked channels (time-sensitive, auto-approved for urgency)

Requires user confirmation (write operations, network access, side effects):

• bash -- arbitrary command execution
• write_file -- creates or overwrites files
• edit_file -- modifies file content
• web_fetch -- fetches a URL (SSRF-protected, but makes network requests)
• web_search -- web search via self-hosted SearxNG (makes network requests)
• task_agent -- spawns an autonomous sub-agent

Note: The JSON schema metadata key auto_approve controls membership in TASK_AUTO_TOOLS (used for task agent sub-sessions). The actual runtime approval behavior is determined by the needs_approval field set in each _prepare_* method on ChatSession. These two mechanisms can differ.

---

Primary Key Fallback

When the model sends a bare string instead of a JSON object as tool arguments (common with smaller models), the primary_key mapping rescues the call:

Model sends:  bash("ls -la")
              raw_args = "ls -la"   (not valid JSON)

PRIMARY_KEY_MAP["bash"] = "command"
Result:       args = {"command": "ls -la"}

Every tool defines a primary_key. The mapping is:

Tool	primary_key
bash	command
read_file	path
write_file	content
edit_file	old_string
search	query
web_fetch	url
web_search	query
task_agent	prompt
memory	name
recall	query
notify	message
read_resource	uri
use_prompt	name

---

File Operations

bash

Execute a bash command and return stdout + stderr.

Parameter	Type	Required	Description
command	string	yes	The bash command to execute.
timeout	integer	no	Timeout in seconds (1-600). Omit to use the global tools.timeout setting (typically 120s).
stop_on_error	boolean	no	Enable set -e so the script exits on the first command failure. Default false.

• What it does: Runs the command in a subprocess with a configurable timeout. Commands are sanitized and checked against a blocklist (e.g. rm -rf /). Environment variables containing secrets are scrubbed (*_KEY, *_SECRET, *_TOKEN, etc.).
• Output format: Stdout is returned directly. Stderr lines are prefixed with [stderr] so the model can distinguish them. When the command itself redirects stderr to stdout (2>&1), no prefix is added. Output exceeding 256KB is truncated (head + tail preserved, middle replaced with a truncation notice).
• Auto-approve: No -- requires user confirmation.
• Agent availability: task_agent only.

---

read_file

Read the contents of a file, returning numbered lines for text files or base64-encoded image data for supported image formats.

Parameter	Type	Required	Description
path	string	yes	Absolute or relative file path.
offset	integer	no	Line number to start from (1-based, default: 1). Text files only.
limit	integer	no	Maximum number of lines to read. Omit for full file. Text files only.

• What it does: For text files, reads and returns content with line numbers. For image files (PNG, JPEG, GIF, WebP, BMP, TIFF, ICO), returns image data as multi-part content when the model supports vision, or a text description when it does not. SVG files are read as text. Images larger than 4 MB are rejected. Must be called before edit_file on the same path (the session tracks which files have been read).
• Vision support: Controlled by ModelCapabilities.supports_vision. All commercial OpenAI and Anthropic models have vision enabled. Local models (vLLM, llama.cpp, NIM) default to off — enable via [models.*.capabilities] supports_vision = true in config.toml.
• Auto-approve: Yes.
• Agent availability: task_agent.

---

write_file

Write content to a file, creating it if needed.

Parameter	Type	Required	Description
path	string	yes	Absolute or relative file path.
content	string	yes	The full file content to write.
mode	string	no	"overwrite" (default) replaces the file. "append" adds content to the end.

• What it does: Creates or overwrites (or appends to) the file at the given path. Parent directories are created as needed.
• Auto-approve: No -- requires user confirmation.
• Agent availability: task_agent only.

---

edit_file

Replace exact strings in a file, or apply multiple replacements atomically.

Parameter	Type	Required	Description
path	string	yes	Absolute or relative file path.
old_string	string	no*	The exact text to find and replace.
new_string	string	no*	The replacement text.
near_line	integer	no	Disambiguate when old_string matches multiple locations.
edits	array	no*	Multiple replacements to apply atomically (see below).
replace_all	boolean	no	Replace ALL occurrences of old_string. Cannot combine with near_line or edits.

* Provide either old_string+new_string (single edit) or edits array (batch), not both.

• What it does: Finds old_string in the file and replaces it with new_string. Fails if the string is not found or matches multiple locations (unless near_line or replace_all is provided). Requires a prior read_file or diff_file call on the same path.
• Batch mode: The edits array accepts multiple {old_string, new_string, near_line?} entries applied atomically. All edits are validated before any are applied. Overlapping edits (two entries targeting the same text region) are rejected. Edits are applied in reverse file-position order so character offsets stay stable.
• Replace-all mode: When replace_all is true, all occurrences are replaced via str.replace(). The approval preview shows the occurrence count.
• Auto-approve: No -- requires user confirmation.
• Agent availability: task_agent only.

---

diff_file

Show a unified diff between two files, or between a file and a provided string.

Parameter	Type	Required	Description
path_a	string	yes	Path to the first file.
path_b	string	no	Path to the second file. Mutually exclusive with content_b.
content_b	string	no	String content to compare against path_a. Mutually exclusive with path_b.
context_lines	integer	no	Number of context lines around changes (default 3, max 20).

• What it does: Returns unified diff output using Python's difflib. Binary files (containing null bytes) are rejected with a clear error. Files read through diff_file satisfy edit_file's read guard — you can diff then edit without a separate read_file call. Large diffs are streamed with early cutoff at the tool truncation limit.
• Auto-approve: Yes (read-only).
• Agent availability: task_agent.

---

search

Search file contents for a regex pattern.

Parameter	Type	Required	Description
query	string	yes	Regex pattern (extended regex).
path	string	no	File or directory to search in (default: current directory).

• What it does: Recursively searches for the pattern using grep -rn. Returns matching lines with file paths and line numbers.
• Auto-approve: Yes.
• Agent availability: task_agent.

---

Information

web_fetch

Fetch a URL and extract specific information from it.

Parameter	Type	Required	Description
url	string	yes	The URL to fetch (must start with http:// or https://).
question	string	yes	What to extract or answer from the page content.

• What it does: Fetches the URL, strips HTML to plain text, and uses the LLM to extract the answer to the question from the page content. Protected against SSRF (blocks private/internal IPs).
• Auto-approve: No -- requires user confirmation (makes network requests).
• Agent availability: task_agent.

---

web_search

Search the web using a text query.

Parameter	Type	Required	Description
query	string	yes	The search query.
max_results	integer	no	Max results to return (default 5, max 20).
category	string	no	Search category: general (default), news, it (code/tech), or science. Maps to SearxNG categories; the model picks per query.

• What it does: Searches the web and returns ranked results with titles, URLs, and content snippets. Uses provider-native search when available:
  • Anthropic: Replaced at the API boundary with Anthropic's web_search_20250305 server-side tool. Claude decides when to search; the API executes it and returns results with citations inline. No backend needed.
  • OpenAI search models (gpt-5-search-api): Replaced with web_search_options parameter. The model always searches and returns url_citation annotations.
  • Local/vLLM models: Falls back to a self-hosted SearxNG instance. Set searxng_url in config.toml [tools] or $TURNSTONE_SEARXNG_URL (the docker-compose stack bundles a searxng service and points at it by default). Operators with a custom MCP search server can instead set web_search_backend = "mcp:server:tool".
• Auto-approve: Yes (auto-approved for all tool dispatch paths).
• Agent availability: task_agent.

---

Agent

The tool name uses the _agent suffix — bare task collides with chat-template channel names on some local models.

task_agent

Delegate a general-purpose task to an autonomous sub-agent.

Parameter	Type	Required	Description
prompt	string	yes	Complete task description for the sub-agent.

• What it does: Spawns a sub-agent that inherits the TASK_AGENT_TOOLS set (read, write, edit, search, bash, web tools, memory tools). The sub-agent runs autonomously to completion. Use for work that requires file modifications or command execution.
• Auto-approve: No -- requires user confirmation.
• Agent availability: Top-level only.

---

Memory

memory

Structured persistent memory across sessions with typed, scoped entries.

Parameter	Type	Required	Description
action	string	yes	save, search, delete, or list.
name	string	save/delete	Short snake_case identifier for the memory.
content	string	save	Memory content to store.
description	string	no	Short description for relevance matching (recommended for save).
type	string	no	Memory type: user, project, feedback, or reference. Default: project.
scope	string	no	Memory scope: global, workstream, or user. Default: global.
query	string	search	Search query for finding memories.
limit	integer	no	Max results for search or list. Default: 20.

• What it does: Manages structured persistent memories in the database. Memories persist across sessions, have a type classification (user preferences, project knowledge, feedback, reference material) and a scope (global across all workstreams, private to a workstream, or following a user). Relevant memories are included in the system prompt on startup.
• Auto-approve: Yes.
• Agent availability: Not available to sub-agents (top-level only).

---

recall

Search conversation history for past messages and tool results.

Parameter	Type	Required	Description
query	string	yes	Search term or phrase to find in conversation history.
limit	integer	no	Max results to return (default 20).

• What it does: Searches conversation history across sessions using FTS5 full-text search. Returns matching messages, tool calls, and tool results with timestamps and workstream context.
• Auto-approve: Yes.
• Agent availability: Not available to sub-agents (top-level only).

---

Notifications

notify

Send a notification to a user or channel on an external platform.

Parameter	Type	Required	Description
message	string	yes	Notification content (plain text, max 2000 chars).
username	string	no	Turnstone username — sends to all linked channels.
channel_type	string	no	Platform for direct targeting (discord).
channel_id	string	no	Platform-specific channel or user ID for direct targeting.
title	string	no	Optional short title (rendered as bold prefix).

Provide either username for user-based targeting or channel_type + channel_id for direct targeting. Do not combine both.

• What it does: Sends a notification via the channel gateway's HTTP endpoint (POST /v1/api/notify). The server queries the services table for healthy channel gateways, authenticates with a service JWT (aud: turnstone-channel), and delivers to the first healthy gateway. On failure, retries up to 2 additional times with backoff (1s, 3s). Rate-limited to 5 notifications per turn (counter only increments on success).
• Auto-approve: Yes — notifications are time-sensitive and auto-approved so the model can alert users urgently.
• Agent availability: task_agent.

See Channel Integrations: Notifications for the full delivery flow, service registry details, and security measures.

---

watch

Set up periodic polling of a shell command within the current workstream. Results are injected back into the conversation as synthetic user messages, triggering the model to respond and act. Use for monitoring CI/CD pipelines, PR reviews, deployments, file changes, etc.

Parameter	Type	Required	Description
action	string	yes	create, list, or cancel.
command	string	create	Shell command to poll periodically.
poll_every	string	no	Poll interval as duration (30s, 5m, 1h). Default: 5m.
stop_on	string	no	Python expression for stop condition (see below). Omit for change detection.
name	string	create	Human-readable watch name (e.g. pr-review). Used as identifier for cancel.
max_polls	integer	no	Max poll cycles before auto-cancel. Default: 100.

Actions:

• create — Start a new watch. Requires approval (same as bash — runs shell commands). Persists to the watches table; the server-level WatchRunner daemon polls every 15 seconds for due watches.
• list — Show all active watches in this workstream. Auto-approved.
• cancel — Stop a watch by name or ID prefix. Auto-approved.

Stop condition DSL — The stop_on parameter accepts a Python expression evaluated after each poll. Available variables:

Variable	Type	Description
output	str	stdout (+stderr) of the command.
data	Any	json.loads(output), or None if not valid JSON.
exit_code	int	Process exit code.
prev_output	`str	None`
changed	bool	True if output differs from previous poll.

Safe builtins: len, str, int, float, bool, abs, min, max, any, all, isinstance, sorted. No import, open, exec, or eval. Security model: equivalent to bash — the model already has shell access.

Examples:

data["state"] == "MERGED"
"error" in output
exit_code != 0
changed and "ready" in output.lower()
data.get("mergedAt") is not None

Lifecycle:

1. Model calls watch(action="create", ...) — persisted to SQLite.
2. WatchRunner daemon polls for due watches every 15s.
3. Each poll runs the command, evaluates the condition.
4. When the condition fires (or max polls reached), the result is injected as a synthetic user message and the watch auto-cancels.
5. If the workstream was evicted, it is restored before injection.
6. Watches survive server restart (overdue watches fire once on recovery).

Constraints:

• Max 5 active watches per workstream.

• Poll interval: 10s–24h.

• Output truncated at 64 KB.

• Max 5 consecutive watch dispatches per worker thread (depth guard).

• Duplicate names rejected within the same workstream.

• Auto-approve: create requires approval; list and cancel are auto-approved.

• Agent availability: Main session only — not available to task sub-agents.

See Watch Architecture for the full poll → evaluate → dispatch flow.

---

skill

Discover and activate skills at runtime during a conversation. The model can search for available skills and load one by name, replacing the current active skill. This enables model-driven skill selection without requiring the user to pre-configure skills at workstream creation.

Parameter	Type	Required	Description
action	string	yes	load or search.
name	string	load	Skill name to activate.
query	string	no	Search query for finding skills (for search action).

Actions:

• load — Activate a skill by name. Calls set_skill() which handles content rendering with {{model}}/{{ws_id}}/{{node_id}} variables, system message reinitialization, and config persistence. Returns the skill name, description, and security risk level. Warns on high/critical risk level.

• search — Find available skills by query. Uses BM25 relevance ranking over name, description, tags, and category (same BM25Index used by memory relevance and tool search). Returns up to 10 results with name, description, category, risk level, and activation type.

• Auto-approve: load requires approval (changes session behavior); search is auto-approved (read-only).

• Agent availability: Main session only — not available to task sub-agents.

---

Summary Table

Tool	Category	Auto-approve	task_agent	primary_key
bash	File Ops	No	Yes	command
read_file	File Ops	Yes	Yes	path
write_file	File Ops	No	Yes	content
edit_file	File Ops	No	Yes	old_string
search	File Ops	Yes	Yes	query
web_fetch	Info	No	Yes	url
web_search	Info	No	Yes	query
task_agent	Agent	No	No	prompt
memory	Memory	Yes	No	name
recall	Memory	Yes	No	query
notify	Notify	Yes	Yes	message
watch	Monitor	No (create)	No	command
read_resource	MCP	No	Yes	uri
use_prompt	MCP	No	Yes	name
skill	Skills	No (load)	No	name
tool_search	Search	Yes	No	query

---

Dynamic Tool Search

When many MCP tools are connected, the total tool count can grow large enough to consume significant context window tokens and reduce model accuracy. Dynamic tool search addresses this by deferring tools the model is unlikely to need on the current turn and letting it search for them on demand.

Three-tier approach

Tool search uses the best available mechanism for each provider:

1. Anthropic (native) -- Models that support it receive defer_loading: true on deferred tool definitions plus the tool_search_tool_bm25 server-side search tool. Anthropic's API handles search and expansion transparently.

2. OpenAI GPT-5.4+ (native) -- Models with hosted tool search receive defer_loading: true on deferred definitions. The API handles search internally.

3. vLLM / llama.cpp / NIM (client-side BM25) -- A synthetic tool_search function tool is injected into the tool list. When the model calls it, _exec_tool_search() runs a pure-Python BM25 index over tool names and descriptions, then expands the matched tools into the visible set.

Configuration

Tool search is configured in config.toml under the [tools] section:

[tools]
search = "auto"           # "auto", "on", or "off"
search_threshold = 20     # minimum total tool count to activate
search_max_results = 5    # max tools returned per search call

CLI flags override the config file:

• --tool-search {auto,on,off} -- force tool search on or off, or let turnstone decide based on threshold (default: auto).
• --tool-search-threshold N -- minimum tool count to activate (default: 20).
• --tool-search-max-results N -- max results per search (default: 5).

How it works

1. Threshold check: At session startup, if the total tool count (built-in + MCP) is below the threshold, tool search stays off and all tools are sent to the model directly.

2. Partitioning: When active, tools are split into two sets:

   • Always-on -- the built-in tools present in the current session (interactive sessions currently have 16; BUILTIN_TOOL_NAMES is the 28-tool built-in union). These are always visible to the model.
   • Deferred -- all MCP tools. These are not sent in the tool list unless the model searches for them.

3. Search and expand: When the model calls tool_search (client-side) or the provider's native search returns results, the matched tools are added to the visible set via expand_visible(). Once expanded, a tool stays visible for the remainder of the session.

4. Multi-turn persistence: Expanded tools are never removed. This avoids confusing the model when it references a tool it discovered in an earlier turn.

Agent exemption

Task sub-agents do not use tool search. They operate on the scoped tool set (TASK_AGENT_TOOLS) with MCP tools merged in. Tool search is only active for the top-level session, where the model can interactively search for tools it needs.

---

MCP Tools (External)

See also: MCP Architecture diagram

Turnstone supports the Model Context Protocol (MCP) for connecting external tool servers — GitHub, databases, filesystems, or any MCP-compatible service.

How it works

1. Configuration: MCP servers are defined in config.toml under [mcp.servers.*] sections, or via a standard MCP JSON config file (--mcp-config).

2. Discovery: At startup, MCPClientManager connects to each configured server (via stdio subprocess or HTTP), performs the MCP initialize handshake, and calls tools/list to discover available tools. During the handshake, the manager checks each server's capabilities for tools.listChanged support (push notifications).

3. Schema conversion: Each MCP tool's inputSchema is converted to OpenAI function-calling format. The tool name is prefixed: mcp__{server}__{tool}.

4. Merging: MCP tools are appended after the 16 built-in tools via merge_mcp_tools(). Built-in tools appear first, giving them natural LLM priority. When dynamic tool search is active, MCP tools are deferred rather than directly visible -- the model discovers them via search as needed (see Dynamic Tool Search above).

5. Dispatch: When the LLM calls an MCP tool, _prepare_mcp_tool() builds a generic approval preview and _exec_mcp_tool() calls MCPClientManager.call_tool_sync(), which dispatches the call to the background asyncio event loop.

Approval behavior

MCP tools require user approval by default (needs_approval: True). turnstone does not auto-approve MCP tools based on their schema, since it cannot guarantee that external tools are read-only. However, global overrides such as --skip-permissions will auto-approve all tools, including MCP tools. The interactive "Always" button adds specific tool types to the per-tool auto-approve set. The web UI and server use approval_label for MCP tools, giving per-prompt/per-resource granularity. The CLI uses func_name, which gives per-tool-type granularity (e.g., all use_prompt calls).

Sub-agent availability

MCP tools are available to:

• Main session — full access
• Task sub-agents — via self._task_tools (merged list)

Naming convention

MCP tool names follow the pattern mcp__{server}__{original}:

• mcp__github__search_repos — search_repos tool from github server
• mcp__postgres__query — query tool from postgres server

Server names must not contain __ (double underscore), which is reserved as the delimiter. Servers with __ in their name are rejected at connection time.

Configuration

TOML (~/.config/turnstone/config.toml):

[mcp.servers.github]
command = "npx"
args = ["-y", "@modelcontextprotocol/server-github"]

[mcp.servers.github.env]
GITHUB_TOKEN = "ghp_..."

[mcp.servers.remote]
type = "http"
url = "https://mcp.example.com/mcp"

JSON (standard mcpServers format, via --mcp-config):

{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {"GITHUB_TOKEN": "ghp_..."}
    }
  }
}

Introspection

Use the /mcp slash command to list all connected MCP tools:

/mcp
MCP tools (3):
  mcp__github__search_repos  [MCP: github] Search GitHub repositories
  mcp__github__create_issue  [MCP: github] Create a GitHub issue
  mcp__postgres__query       [MCP: postgres] Run a SQL query

Dynamic tool refresh

MCP tool lists stay up-to-date without restart through two mechanisms:

1. Push notifications -- MCP servers that declare tools.listChanged: true in their capabilities send notifications/tools/list_changed when their tool list changes. MCPClientManager registers a message_handler on each ClientSession that triggers an immediate refresh for that server.

2. Manual -- /mcp refresh re-fetches tools from all servers immediately. /mcp refresh <server> targets a single server. If a server has disconnected, manual refresh attempts reconnection. The console admin panel exposes the same controls (refresh / reconnect buttons per server) for cluster-wide fan-out.

When tools change, MCPClientManager rebuilds its merged tool list using copy-on-write (new list/dict objects assigned atomically) and notifies all active ChatSession instances via registered listener callbacks. Each session rebuilds its _tools, _task_tools, and reconstructs its ToolSearchManager (if active), preserving the set of previously expanded (discovered) tools.

/mcp refresh
MCP refresh complete:
  github: +1 added
    + mcp__github__create_pr
  postgres: no changes

/mcp refresh github
MCP refresh complete:
  github: no changes

---

MCP Resources

MCP servers can expose resources -- named data items (files, database rows, API responses) addressable by URI. turnstone discovers resources at startup and makes them available to the model via the read_resource built-in tool.

Discovery

During the MCP initialize handshake, MCPClientManager checks each server's capabilities for the resources capability. For servers that declare it:

1. list_resources fetches static resources (fixed URIs).
2. list_resource_templates fetches URI templates (parameterized patterns like db://tables/{table}/rows/{id}).

Both are stored as {uri, name, description, mimeType, server} dicts and merged into a unified catalog.

Resource catalog in system message

The first 50 resources are injected into the system message as an XML-delimited block so the model knows what URIs are available:

<mcp-resources>
  file:///project/README.md  Project readme
  db://users/schema  User table schema
</mcp-resources>
Use read_resource(uri='...') to access the resources listed above.

read_resource tool

Parameter	Type	Required	Description
uri	string	yes	The resource URI to read.

• What it does: Reads the resource from its MCP server via MCPClientManager.read_resource_sync(). Returns text content for text resources or base64-encoded data for binary resources. Output is truncated by the standard tool output limiter.
• Auto-approve: No -- requires user confirmation (reads external data).
• Agent availability: task_agent.

Capability guards

The read_resource tool schema is always loaded (it is a built-in JSON schema), but resource discovery only runs for servers that declare the resources capability. Servers without the capability contribute zero resources to the catalog.

Refresh

Resource lists stay current through the same three-tier mechanism as tool lists:

1. Push -- Servers declaring resources.listChanged: true send notifications/resources/list_changed, triggering an immediate refresh.
2. Periodic -- Servers without push are polled on the configured refresh interval (default 4 hours, same timer as tools).
3. Manual -- /mcp refresh re-fetches resources alongside tools.

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MCP Prompts

MCP servers can also expose prompts -- reusable message templates with optional arguments. turnstone discovers prompts at startup for servers that declare the prompts capability.

Discovery

Prompt discovery mirrors resource discovery: list_prompts is called during the initialize handshake. Each prompt is stored with its prefixed name (mcp__{server}__{prompt}), description, and argument schema.

use_prompt tool

Parameter	Type	Required	Description
name	string	yes	The prompt name (e.g. mcp__server__prompt_name).
arguments	object	no	Key-value argument pairs for the prompt. Values must be strings.

• What it does: Invokes an MCP prompt by name via MCPClientManager.get_prompt_sync(), expanding it into messages. Returns the expanded prompt content formatted as [role]: content blocks joined with blank lines. The prompt catalog is listed in the system message so the model knows which prompts are available. Output is truncated by the standard tool output limiter.
• Auto-approve: No -- requires user confirmation (invokes external prompt servers).
• Agent availability: task_agent.

Invocation

MCPClientManager.get_prompt_sync() calls the server's get_prompt method with the provided arguments and returns the expanded messages. The use_prompt built-in tool exposes this to the model as a function call.

Governance Sync

Discovered MCP prompts are automatically synced into the prompt_templates table (which stores skills) as first-class governed skills:

• Origin tracking: MCP-sourced skills have origin="mcp" and mcp_server set to the server name. Manual skills have origin="manual".
• Read-only: MCP-sourced skills are readonly=True. The admin API returns 403 on update/delete attempts. The admin UI disables edit/delete buttons and shows an origin badge.
• Precedence: If a manual skill and MCP prompt share the same name, the manual skill wins and the MCP prompt is skipped (with a log warning).
• Lifecycle: Skills are created on connect, updated on prompt list refresh, and removed when the MCP server no longer exposes the prompt. The sync runs automatically on connect, on PromptListChangedNotification, and on manual /mcp refresh.
• Schema: Migration 009 adds origin, mcp_server, and readonly columns to the prompt_templates table.

The use_prompt tool allows the model to invoke any discovered MCP prompt at runtime. A catalog of up to 30 prompts is injected into the system message inside <mcp-prompts> XML tags so the model can discover available prompts.

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MCP UI Visibility

MCP server, resource, and prompt counts are surfaced across the UI:

• Server /health endpoint: Returns mcp.servers, mcp.resources, mcp.prompts when MCP is configured
• Server UI: Magenta status badge in the header showing server count, with resource/prompt counts in tooltip
• Console cluster status bar: MCP metrics (servers/resources/prompts) with magenta LED dot indicator, shown after a divider from workstream metrics
• Console node detail: Per-node MCP summary showing server, resource, and prompt counts
• Console collector: Aggregates MCP counts across all nodes in the cluster overview

MCP indicators use the --magenta design token for consistent theming across light and dark modes.