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MBaranekTech

PDF RAG MCP Server

by MBaranekTech

pdf_search

Find relevant information in PDFs by submitting a natural language query, returning the most similar text passages. Supports filtering to a single document for targeted searches.

Instructions

Semantic search across all ingested PDFs. Returns the most relevant text chunks ranked by similarity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of results to return (1-20, default 5).
queryYesNatural language search query.
doc_idNoOptional document ID to restrict search to a single PDF.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the return type (text chunks) and ranking behavior (by similarity), and notes the scope ('all ingested PDFs'). However, it does not mention read-only nature, pagination, or any search limitations (e.g., text-only vs. images), which are justifiable gaps for a search tool.

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 concise sentences, front-loaded with the core purpose ('Semantic search'), and zero wasted words. It clearly conveys the key behavior while staying efficient.

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?

An output schema exists, so return-value details are covered structurally. The description covers the search scope, ranking, and the fact that it searches across all PDFs. Minor gaps like pagination or filtering behavior are acceptable given the schema coverage and presence of an output schema.

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 all three parameters (query, limit, doc_id) described in the input schema. The description adds no additional parameter semantics, so the baseline of 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?

States a specific verb ('search') and resource ('all ingested PDFs'), and specifies the output as ranked text chunks. This distinguishes it from sibling tools like pdf_get_page or pdf_extract_tables, making the purpose unambiguous.

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?

Provides no guidance on when to use this tool versus alternatives. It does not mention when to prefer pdf_get_page, pdf_extract_tables, or other siblings, leaving the agent to infer usage context.

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