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fw-context-mcp

by turbyho
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Python
Local

configure_llm

Set per-project LLM chat and embedding settings for fw-context-mcp in a gitignored local file without changing shared config; test the API connection after saving.

Instructions

Configure LLM settings for the current project.

Writes to <project>/.fw-context/local.toml ONLY (gitignored, per-developer). Does NOT modify the global config or the shared project config.toml. After writing, tests the configuration by making a simple API call (skipped when LLM is disabled).

IMPORTANT: When chat_api_base points to an external host, source code snippets in chat prompts will be sent to that endpoint. Ensure this complies with your organization's data security policies. Consider using local Ollama or an internal API proxy first.

Args: project_root: Project root directory. Auto-detected if omitted. project: Project name or project_id — call list_projects to get them. Use it to ask about a project that is not the project of the current directory. It is an alternative to project_root, which takes a root path. Give one of the two, not both. chat_api_base: Chat API URL (see description for format details). chat_api_key: Bearer token for cloud/proxy APIs. chat_api_format: Override auto-detection: "auto", "ollama", "openai". model: Chat model name. embed_model: Embedding model name (Ollama only). auto_pull: Whether to auto-pull models on 404. stream: Stream chat responses via SSE. True avoids reverse-proxy idle timeouts.

Returns: dict: {status ("ok"|"error"), chat_api (dict — configured, endpoint, format, model), model (str), auto_pull (bool), stream (bool), test_latency_s (float, on success), test_response (str, on success), compliance_warning (str, when chat_api_base is external), message (str)}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoChat model name. None = keep current.
streamNoStream chat responses via SSE (both OpenAI-compatible and Ollama-native). True = send stream:true, consume SSE chunks — avoids reverse-proxy idle timeouts (nginx 60s, Cloudflare 100s). None = keep current setting.
projectNoProject name or project_id — call list_projects to get them. Use it to ask about a project that is not the project of the current directory. It is an alternative to project_root, which takes a root path. Give one of the two, not both.
auto_pullNoAuto-pull models on 404 (Ollama only). False for intranet.
embed_modelNoEmbedding model name (Ollama only). None = keep current.
chat_api_keyNoAPI key for cloud/proxy APIs. None for local/no-auth.
project_rootNoProject root. Auto-detected if omitted. This field also accepts a project name or a project_id, but project is the clear field for those.
chat_api_baseNoChat API URL. None = use local Ollama for chat. Auto-detects format: :11434 or /api/generate -> Ollama, /v1 or bare host -> OpenAI-compatible. Examples: 'https://api.deepseek.com/v1' (DeepSeek), 'http://localhost:4000' (LiteLLM), 'http://localhost:8080/v1' (llama.cpp). WARNING: external URLs send source code to that host.
chat_api_formatNoFormat override: 'auto' (default), 'ollama', or 'openai'.auto

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv0.30.0
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / project
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Project name or project_id — call list_projects to get them. Use it to ask about a project that is not the project of the current directory. It is an alternative to project_root, which takes a root path. Give one of the two, not both.",
+  "title": "Project"
+}
    • changedInput schema / properties / project_root / description
      Previous value: -"Project root. Auto-detected if omitted."New value: +"Project root. Auto-detected if omitted. This field also accepts a project name or a project_id, but project is the clear field for those."
  2. Addedv0.25.3

TDQS

A4.5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden and meets it: it discloses the write location, that it does NOT modify global/shared config, that it makes a test API call, that the call is skipped when LLM is disabled, and that external chat_api_base URLs send source-code snippets to that host.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is structured with a clear opening, security warning, Args list, and Returns dict. Despite covering nine parameters, it stays dense and front-loads the most important behavioral facts before the parameter list.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the absence of an output schema and annotations, this is complete: it explains side effects, conditional test behavior, security implications, and the full return dict shape with keys for success/error cases. An agent can invoke it without needing additional context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The Args block is mostly a condensed restatement of the schema's parameter descriptions; it adds a 'Bearer token' qualifier for chat_api_key, but for chat_api_base it even defers to 'description for format details' without including those details in the description body.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific action and resource — 'Configure LLM settings' — and immediately clarifies the precise mutation target: 'Writes to <project>/.fw-context/local.toml ONLY'. It also states what it does not affect, which clearly separates it from the surrounding read/search sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives clear context for when to call it (per-developer LLM configuration) and explicit non-targets (global config, shared config.toml). It also points to list_projects for project IDs and warns to prefer local Ollama/internal proxies; it stops short of naming an alternative configuration tool because none exists among the siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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