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llm-wiki-mcp

HTTP MCP server providing wiki knowledge base tools (query and ingest), powered by llm-wiki-agent skills via the Claude Agent SDK.

Architecture

MCP Client (Claude Code, etc.)
    ↓ HTTP / stdio
llm-wiki-mcp (FastMCP server)
    ↓ Claude Agent SDK (query())
Claude Code agent session
    ↓ auto-loaded skills
llm-wiki-agent (.claude/commands/)
    ↓ /wiki-ingest, /wiki-query
Wiki markdown files (entities, concepts, sources, syntheses)

The MCP server is a thin layer. The actual knowledge extraction, entity/concept page creation, cross-referencing, and contradiction detection are all handled by the llm-wiki-agent skill running inside a Claude Agent SDK session.

Related MCP server: RAG MCP Server

Quick Start

# Install dependencies
uv sync

# Clone llm-wiki-agent skills
git clone --depth 1 https://github.com/SamurAIGPT/llm-wiki-agent.git skills/llm-wiki-agent

# Set your Anthropic API key
export ANTHROPIC_API_KEY=sk-ant-...

# Start the server (HTTP mode, default port 8080)
uv run llm-wiki-mcp

# Or use stdio mode
uv run llm-wiki-mcp --transport stdio

Tools

ingest

Ingest a document (file path or URL) into a named wiki. The llm-wiki-agent skill handles reading, converting, extracting knowledge, and building cross-referenced pages.

ingest(wiki_name="my-research", source="raw/papers/attention-is-all-you-need.md")
ingest(wiki_name="my-research", source="report.pdf")

query

Query a wiki with a natural language question. The skill searches all wiki pages and synthesizes an answer with [[wikilinks]].

query(wiki_name="my-research", question="What are the main approaches to attention?")

Docker

docker build -t llm-wiki-mcp .
docker run -e ANTHROPIC_API_KEY=sk-ant-... -p 8080:8080 -v ./wikis:/home/agent/wikis llm-wiki-mcp

Optional: configure custom base URL or models:

docker run \
  -e ANTHROPIC_API_KEY=sk-ant-... \
  -e ANTHROPIC_BASE_URL=https://custom.anthropic.com \
  -e ANTHROPIC_SMALL_FAST_MODEL=claude-haiku-4-20250514 \
  -e ANTHROPIC_DEFAULT_SONNET_MODEL=claude-sonnet-4-20250514 \
  -e ANTHROPIC_DEFAULT_OPUS_MODEL=claude-opus-4-20250514 \
  -e ANTHROPIC_DEFAULT_HAIKU_MODEL=claude-haiku-4-20250514 \
  -p 8080:8080 \
  -v ./wikis:/home/agent/wikis \
  llm-wiki-mcp

Available Tools

2 tools
ingestA

Ingest a document into a wiki knowledge base.

The source is fetched (URL or local path) and saved to the wiki's raw/ directory, then the llm-wiki-agent /wiki-ingest skill processes it — extracting entities, concepts, and cross-references into structured wiki pages.

Args: wiki_name: Name of the wiki to ingest into (created if it doesn't exist). source: A local file path or URL to ingest.

ParametersJSON Schema
NameRequiredDescriptionDefault
wiki_nameYes
sourceYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, description covers main behaviors: fetching source, saving, processing by agent. Mentions wiki_name creates if doesn't exist. Could add more on side effects or failure modes, but adequate.

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?

Two well-structured paragraphs: purpose and process followed by Args list. No redundant sentences, efficient and clear.

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

Completeness4/5

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

Given output schema exists, return values need not be described. Input and process are explained. Lacks details on expected output after ingestion, but still sufficient without annotations.

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

Parameters5/5

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

Schema coverage is 0%, so description fully explains both parameters: wiki_name (target wiki, auto-created) and source (local path or URL). Adds meaning beyond schema type 'string'.

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 clearly states the tool's function: 'Ingest a document into a wiki knowledge base.' It details the process steps (fetch, save, process) and distinguishes from the sibling 'query' tool by focusing on ingestion.

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

Usage Guidelines3/5

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

Usage is implied (add documents to wiki) but no explicit guidance on when to use this vs. alternatives like 'query'. No when-not-to-use or prerequisites stated.

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

queryA

Query a wiki knowledge base and get an AI-synthesized answer.

Uses the llm-wiki-agent /wiki-query skill to search wiki pages and synthesize a comprehensive answer with [[wikilinks]] citations.

Args: wiki_name: Name of the wiki to query. question: The question to ask about the wiki's knowledge.

ParametersJSON Schema
NameRequiredDescriptionDefault
wiki_nameYes
questionYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A3.8/5.0
Behavior3/5

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

Describes the process (searches wiki pages, synthesizes answer with citations) but lacks explicit statements about side effects or safety (e.g., read-only, no destructive actions). With no annotations, more explicit behavioral disclosure would improve clarity.

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?

Extremely concise – three sentences plus a two-item Args list. Front-loaded with purpose. Every sentence adds value; no redundancy or fluff.

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

Completeness4/5

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

Has output schema, so return values are documented. Covers wikilinks citations. Missing details on prerequisites, error handling, or exact wiki name format, but given the simple two-parameter input, the description is nearly complete.

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

Parameters4/5

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

Schema description coverage is 0%, but the description's Args section provides clear, concise explanations for both parameters ('wiki_name: Name of the wiki to query', 'question: The question to ask'), adding meaning beyond the bare parameter names.

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 clearly states the tool queries a wiki knowledge base and returns an AI-synthesized answer with wikilinks citations. It distinguishes from sibling tool 'ingest' by focusing on querying rather than adding data.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives. The sibling tool 'ingest' is mentioned but not contrasted. There is no 'when not to use' or prerequisite information.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 2 tool updatesv0.1.0
    • First observedingest
    • First observedquery
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TDQS

A3.9/5.0
Disambiguation5/5

The two tools, 'ingest' and 'query', have clearly distinct purposes: one adds documents to the wiki, the other retrieves information. There is no ambiguity or overlap in their functionality.

Naming Consistency5/5

Both tool names follow a single-verb pattern that directly describes their action ('ingest' and 'query'), making them consistent and predictable.

Tool Count3/5

With only two tools, the server feels under-scoped for a wiki knowledge base. While the tools cover core ingest and query actions, a more complete server would include additional management tools.

Completeness2/5

The server lacks essential operations such as listing wikis, deleting wikis, or updating ingested content. Users have no way to manage the wiki beyond ingesting and querying, which are significant gaps.

Maintenance

ActivityStale
ResponsivenessSyncing

Resources

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