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pdf-knowledge-mcp

pdf-knowledge-mcp

by tycket033-tech
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

search_knowledge

Search a local PDF knowledge base for development questions, keywords, or error symptoms. Returns ranked text chunks with source references using semantic TF-IDF retrieval.

Instructions

Search the local PDF development knowledge base with semantic-style TF-IDF vector retrieval and return ranked chunks with source references.

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_knowledge"
  }
}

Input Schema

NameRequiredDescriptionDefault
tagsNoOnly search chunks that contain all of these tags.
limitNoMaximum number of chunks to return.
queryYesPDF development question, keyword, API name, error symptom, or concept to search for.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

Description mentions 'semantic-style TF-IDF vector retrieval' and 'ranked chunks' but doesn't detail limitations, required permissions, or behavior for edge cases. No annotations provided to offset this.

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?

Single, concise sentence with no extraneous information. Every phrase adds value.

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?

With 3 parameters and no output schema, the description covers the core purpose and return format (ranked chunks). Could be improved by noting range of acceptable queries or performance considerations.

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% with good descriptions for each parameter (query, tags, limit). The description doesn't add significant meaning beyond what the schema already provides, so baseline 3 is appropriate.

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?

Description clearly states the tool searches a local PDF knowledge base using semantic-style TF-IDF vector retrieval and returns ranked chunks with source references, distinguishing it from siblings like ask_pdf_expert or ingest_document.

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?

Implied usage for searching PDF development knowledge, but no explicit guidance on when to use this tool versus ask_pdf_expert (which likely handles interactive Q&A) or ingest_document (which ingests documents).

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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