Skip to main content
Glama
302ai

302AI File Parser MCP Server

by 302ai

parseFileToText

Extract text content from files by providing a URL, supporting formats like PDF, DOCX, CSV, HTML, and more for processing in Claude Desktop.

Instructions

Provide a file url, parse the file to text, return the text as a string.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesPublic URL of the source file, supports pdf/docx/csv/txt/html/odt/rtf/epub/md/xml/xsl/pptx/potx/js/cs
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions parsing and returning text, but doesn't disclose critical behavioral traits: supported file formats (covered in schema), parsing limitations (e.g., large files, encoding issues), error handling, or performance characteristics. The description is too vague about how parsing works and what happens in edge cases.

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?

The description is concise and front-loaded in a single sentence: 'Provide a file url, parse the file to text, return the text as a string.' It efficiently states the core functionality without unnecessary words. However, it could be slightly more structured by separating input, action, and output more clearly.

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?

Given no annotations and no output schema, the description is incomplete for a tool that performs file parsing. It doesn't explain the return value format beyond 'as a string' (e.g., structured text, encoding), doesn't mention potential errors or limitations, and lacks details on behavioral aspects. For a tool with 1 parameter but complex underlying functionality, this leaves significant gaps.

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 description coverage is 100%, so the schema fully documents the single parameter 'url' with its type and supported formats. The description adds no additional meaning beyond what's in the schema—it merely repeats 'Provide a file url' without extra context. This meets the baseline of 3 when schema coverage is high.

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 clearly states the tool's purpose: 'parse the file to text, return the text as a string.' It specifies the action (parse) and resource (file), and mentions the input (file url) and output (text string). However, it doesn't distinguish from siblings since none exist, so it can't achieve the full 5-point differentiation.

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?

The description provides minimal usage guidance: 'Provide a file url' indicates when to use it, but offers no context about when not to use it or alternatives. With no sibling tools, it can't specify alternatives, but it lacks any prerequisites, limitations, or error conditions that would help an agent decide appropriateness.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/302ai/302_file_parser_mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server