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shaiksameer667ss-prog

Biomedical Research MCP Server

extract_pdf_text

Extracts text from PDFs and stores it in research_content and research_pages for use in biomedical research workflows.

Instructions

Extract text from a PDF and save it into research_content and research_pages.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
document_idYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full burden of disclosing side effects. It does state that the tool writes into two specific stores, which is helpful. However, it does not say whether the operation overwrites existing content, creates duplicates, requires special permissions, or behaves on failure.

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?

A single, front-loaded sentence states the action first and the storage target second. There is no redundant text or filler; every part of the sentence is informative.

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

Completeness2/5

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

This is a mutation tool with no annotations and no output schema, so the return/verification behavior is unspecified. It also lacks details on what happens if the document_id is invalid or the PDF cannot be parsed. The side-effecting behavior is identified, but an agent still cannot predict the result or how to confirm success.

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

Parameters2/5

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

Schema description coverage is 0%, and the description does not explain the sole required parameter document_id. The parameter is reasonably inferable from its name/title, but the description adds no value beyond the schema, so it only partially compensates.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action (extract text from a PDF) and the resulting side effect (saving into research_content and research_pages). It distinguishes itself from get/search/list siblings by describing an ingest operation, though it does not explicitly name sibling alternatives, so it falls just short of a 5.

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?

The intended use case is implied: call this when a PDF's text needs to be extracted and persisted for later research. However, there is no explicit when-to-use guidance, no prerequisites, and no mention of when a sibling such as add_research_document would be more appropriate.

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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