PDF Slim — PDF Compression for AI

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

With no annotations, the description carries full responsibility for behavioral disclosure. It reveals the internal process (iterating quality/resolution, re-encoding as JPEG), rate limits (20 calls/day per IP), file size limits (10MB), and even mentions the need for an API key for larger files. This is thorough and goes beyond a simple 'compress' statement.

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 somewhat long but front-loaded with the core purpose and method. Each sentence adds useful information (usage, limitations, alternatives), though some might find the API key and website details excessive. It remains efficient and focused.

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 tool with no annotations and no output schema, the description is remarkably complete. It covers purpose, method, usage context, file size and rate limits, alternatives for larger files, and even where to obtain API keys. It does not explicitly describe the return value, but for a compression tool this is implied and acceptable.

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 provides 100% coverage of parameter descriptions, so the baseline is 3. The description adds value by explaining how quality and target_size interact (iterating quality/resolution) and gives concrete examples (e.g., 500KB, 1MB), enriching the semantic understanding beyond the schema.

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 compresses PDFs to reduce size, and explains the method (rendering pages and re-encoding as JPEG). However, it does not distinguish itself from the sibling tool 'optimize_pdf', so it lacks explicit sibling differentiation.

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 explicitly says 'Use this when a user wants to shrink a PDF' and provides examples (500KB, 1MB, 'smaller'). It also gives clear alternatives for larger files (API key or the free website), effectively covering when to use and when not to use this tool.

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

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.

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

With no annotations, the description carries the burden. It discloses lossless behavior, rate limits (20 calls/day/IP), file size cap (10MB), and auth requirements (API key for larger limits). Missing return value details, but substantial behavioral 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 compact and front-loaded: purpose first, then usage guidance, then limitations/auth. Every sentence adds value without redundancy.

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 simple one-parameter tool with no output schema or annotations, the description provides sufficient context on purpose, usage, and constraints. It lacks explicit return value description, but the expected output (optimized PDF) is implicit and reasonably inferable.

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?

Schema covers the single parameter (file_base64) at 100%, and the description adds meaningful constraints: file size limit and API key requirement for larger files, which helps agents validate inputs before calling.

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 it performs lossless PDF optimization with specific techniques (garbage collection, stream re-compression) and explicitly notes no quality loss. This distinguishes it from likely lossy compression tools.

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?

Provides clear usage context: good for PDFs with redundant data, and notes effect is small on already-optimized files. However, it does not explicitly compare with sibling tools like compress_pdf or state when not to use it.

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