Inspect a PDF: page count, file size, password protection. Use before deciding how to process a PDF. Free anonymous tier: 20 calls/day per IP. More calls need an API key (https://ko-fi.com/yugutou/shop).
| Name | Required | Description | Default |
|---|---|---|---|
| file_base64 | Yes | The PDF file contents, base64-encoded. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full transparency burden. It discloses rate limits and the need for an API key beyond the free tier, and it names the exact inspection results. It does not explicitly state that the PDF is unmodified, but 'inspect' strongly implies a read-only operation.
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 three short sentences, front-loaded with the core purpose, followed by usage context and auth/rate-limit details. Every sentence earns its place, and there is no repetition of schema information or filler.
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?
For a single-parameter inspection tool with no output schema, the description is complete: it states what the tool returns, when to use it, and any access limitations. The listed outputs (page count, file size, password protection) sufficiently cover the expected return values.
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 schema already fully describes file_base64 as 'The PDF file contents, base64-encoded,' so the description adds no additional parameter-specific meaning. Baseline of 3 is appropriate since there is no ambiguity or undocumented aspect to compensate for.
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 opens with 'Inspect a PDF' and concretely lists what the tool returns (page count, file size, password protection), making its purpose unmistakable. This metadata-gathering role is clearly distinct from sibling tools compress_pdf and optimize_pdf.
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?
It explicitly advises 'Use before deciding how to process a PDF,' providing a clear when-to-use signal. It does not explicitly name alternatives or state when not to use it, but the inspect/compress/optimize verb contrast makes the intended workflow obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
compress_pdf and optimize_pdf both reduce file size, which creates some overlap, but the descriptions clearly distinguish lossy re-encoding from lossless optimization. inspect_pdf is entirely separate.
All tool names follow a consistent verb_noun pattern: compress_pdf, inspect_pdf, optimize_pdf. This is uniform and predictable.
With only three tools, the set is tightly scoped to the server's stated purpose of PDF compression and optimization. Every tool serves a clear function without unnecessary bloat.
The server covers the core operations for PDF size management: inspect to assess the file, compress for lossy size reduction, and optimize for lossless cleanup. No obvious gaps for its intended use case.
