llm-wiki-mcp

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Python

Local

query

Query a wiki knowledge base and receive an AI-synthesized answer with citations from relevant wiki pages.

Instructions

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.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "wiki_name": "<wiki_name>",
      "question": "<question>"
    },
    "name": "query"
  }
}

Input Schema

Name	Required	Description	Default
`wiki_name`	Yes
`question`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jun 4, 2026

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.

Deploy Server

Other Tools

ingestA
queryA