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LLM Wiki MCP Server

LLM Wiki MCP Server

by Delagund
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

search_wiki

Performs hybrid semantic and lexical search across a Markdown knowledge base, filtering by project and combining vector and FTS5 results using reciprocal rank fusion.

Instructions

Busca contexto usando búsqueda semántica híbrida (KNN Vec0 + FTS5) fusionada mediante RRF. Explicación: Si Ollama está disponible, ejecuta tanto búsqueda semántica como léxica, filtrando por proyecto y fusionando resultados usando Reciprocal Rank Fusion (RRF). Si Ollama no está disponible o falla, degrada a búsqueda léxica únicamente.

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": {
      "query": "<query>"
    },
    "name": "search_wiki"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
queryYes
current_projectNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

The description explains the hybrid search algorithm, fallback to lexical-only, and project filtering, providing good behavioral detail beyond the absent annotations.

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 concise with two sentences: first states purpose, second explains behavior. No unnecessary words.

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 the existence of an output schema, the description covers the algorithm and fallback well, though it omits mention of pagination or sorting. It is fairly complete for a search tool.

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?

The description adds context for the current_project parameter (filtering by project) but does not explain limit or query beyond their roles, partially compensating for the 0% schema coverage.

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 performs hybrid semantic and lexical search using RRF, distinct from siblings like list_notes and save_note.

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?

The description implicitly differentiates from siblings by focusing on search, but does not explicitly state when to use this tool over alternatives.

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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