MCP PDF Reader

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool searches and returns matches with context, but lacks details on error handling (e.g., invalid file paths), performance traits (e.g., speed, memory usage), or output format specifics. For a tool with 3 parameters and no annotations, this is a significant gap in transparency.

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

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

The description is a single, efficient sentence that front-loads the core functionality ('search for text in a PDF') and outcome ('return all matches with context'). There is zero waste or redundancy, making it highly concise and well-structured for quick comprehension by an AI agent.

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

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

Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on output format, error cases, or integration with sibling tools. Without annotations or an output schema, more context on behavior and results would improve completeness, but it meets the bare minimum for a search operation.

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

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

The input schema has 100% description coverage, clearly documenting all parameters. The description adds no additional semantic meaning beyond what the schema provides, such as explaining 'context' in the return or usage examples. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, but the description doesn't compensate or enhance parameter understanding.

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

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

The description clearly states the tool's purpose with specific verbs ('search for text') and resource ('in a PDF'), and specifies the outcome ('return all matches with context'). It distinguishes from siblings like 'read_pdf' or 'get_pdf_metadata' by focusing on text search. However, it doesn't explicitly differentiate from potential alternatives like 'read_pdf_pages' for context extraction, keeping it from a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'read_pdf' for full text extraction or 'extract_pdf_image' for non-text content, nor does it specify prerequisites such as file accessibility or performance considerations. This lack of contextual usage information limits its effectiveness for an AI agent.

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