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chatwithyourpdf

Read-only

Chat With Your PDF — Read the text of the user's document — PDFs first and foremost, plus TXT/CSV/Markdown/JSON — so you can chat about it: answer questions, summarize it, or use facts from it in later steps. Business plan only. Returns up to max_chars characters. For Word files: convert with word_to_pdf first, then chat with the PDF. [category: content] [requires the business plan]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fileYesThe document to read
max_charsNoHow much text to return (200-12000)

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, and the description's 'Read the text' is fully consistent (no contradiction). The description adds genuine behavioral context beyond annotations: the business-plan gate, the max_chars truncation limit, and the exact input format scope.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Core information (purpose, formats, use cases) is front-loaded and each clause earns its place. Minor redundancy: 'Business plan only' is repeated by the trailing '[requires the business plan]' tag, and the opener echoes the title.

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?

For a moderate-complexity file-ingestion tool with no output schema, the description covers purpose, formats, plan constraint, truncation limit, and the conversion workflow — enough for correct invocation. The main gap is the exact return shape (plain text vs. structured), which is left inferable.

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?

With 100% schema coverage the baseline is 3, but the description adds value for the `file` parameter by specifying which formats are accepted — something the schema's generic 'The document to read' omits. It also reinforces the max_chars behavior ('Returns up to max_chars characters'), which helps the agent size the request correctly.

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?

Specifies a concrete action (read/extract the text of the user's document), enumerates the supported formats (PDF, TXT, CSV, Markdown, JSON), and ties the tool to clear use cases (answer questions, summarize, use facts later). The chat-oriented framing distinguishes it from sibling extraction/conversion tools like pdf_to_text, and the Word-file exclusion clarifies scope.

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?

Explicitly routes the Word-file edge case to convert_word_to_pdf before using this tool, which is strong when-to/not-to guidance. However, it stops there — it doesn't address alternatives for other scenarios (e.g., pure extraction via pdf_to_text, OCR for scanned PDFs via pdf_ocr), leaving some routing to inference.

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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TDQS

B3.2/5.0
Disambiguation2/5

Multiple tool pairs are near-identical: octopus_mkdir/octopus_make_folder and octopus_move/octopus_move_file are literal duplicates, analyze_hash/generate_hash both compute hashes, convert_word_to_pdf overlaps convert_document, and photo_compress/photo_compress_to_size plus pdf_thumbnails/pdf_to_images have fuzzy boundaries. The descriptions are detailed and cross-reference each other helpfully, but at 144 tools an agent will regularly misselect.

Naming Consistency3/5

The dominant {category}_{verb}_{object} snake_case pattern (pdf_*, photo_*, convert_*, analyze_*, media_*) is largely consistent and predictable. However, outliers like chatwithyourpdf and describe_image break the category-prefix convention, and the octopus namespace mixes bare verbs (read, write, mkdir) with verb_noun forms (make_folder, move_file, search_meta) inconsistently.

Tool Count2/5

144 tools is an extreme count for any MCP server. The broad scope (PDF, photo, video, audio, conversion, analysis, generation, file storage, web, e-sign) justifies some volume, but the count is inflated by batch and inspect variants (pdf_to_excel + batch + inspect), duplicate tools, and overlapping converters. An agent faces an overwhelming selection surface.

Completeness4/5

Per-domain coverage is remarkably deep: PDF spans merge/split/compress/protect/unlock/metadata/OCR/watermark and bidirectional conversion; photo covers editing, format conversion, face handling, OCR, and collage; file storage has full CRUD plus search. Minor gaps exist (no audio transcription, no video metadata editing, no deletion of PDF pages is actually covered via pdf_delete_pages) but the surface has no dead ends for its declared domains.

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