Extract an inclusive, 1-based page range from a PDF into a new PDF. e.g. from=5,to=10 keeps pages 5–10. Returns base64.
| Name | Required | Description | Default |
|---|---|---|---|
| to | Yes | Last page to keep (1-based, inclusive). | |
| Yes | The PDF as a base64 string or https URL. | ||
| from | Yes | First page to keep (1-based, inclusive). |
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?
The description discloses 1-based indexing and return format (base64) but does not mention side effects, whether the original PDF is modified, or behavior on invalid input. Given no annotations, more detail would be helpful.
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?
Two sentences, front-loaded with purpose, followed by an example and return type. No unnecessary words.
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?
Covers core functionality, input parameters implicitly, and return type. Lacks mention of error handling or file size limits, but for a simple tool it is mostly complete.
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 coverage is 100% with parameter descriptions. The description adds an example and clarifies inclusivity, providing some extra context but not substantially 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 extracts a page range from a PDF to a new PDF with a concrete example. It effectively distinguishes from siblings like delete_pages or compress_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?
No guidance on when to use this tool versus alternatives like compress_pdf or delete_pages. The description does not mention prerequisites or typical use cases.
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.
Each tool targets a distinct PDF operation: compression, page deletion, info retrieval, merging, protection, rotation, and splitting. There is no overlap or ambiguity between tool purposes.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., compress_pdf, delete_pages), making it easy to predict functionality from the name.
With 7 tools covering essential PDF manipulations, the count is well-scoped for the domain. Each tool serves a clear purpose without unnecessary redundancy.
The set covers core PDF operations (CRUD for pages, compression, protection, info). Minor gaps exist (e.g., no watermarking or annotation), but the surface is sufficient for typical workflows.
