extract_pdf_text
Extract text content from a base64-encoded PDF document.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | Base64-encoded PDF document |
swiss-army
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
A3.8/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of explaining behavior. It states the core transformation (PDF to text) but does not mention limitations, edge cases, error handling, or the exact return format. This is adequate but not rich in behavioral detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that is front-loaded with the tool's primary function. Every word earns its place, and there is no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one parameter, no nested objects) and lack of output schema, the description is mostly complete. It clearly states the input and the output concept, though it could be slightly stronger by explicitly mentioning return format or error conditions.
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?
The schema describes the single 'pdf' parameter as a base64-encoded PDF, which matches the description exactly. Since schema description coverage is 100%, the description adds no additional semantic meaning beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool extracts text from a PDF, which is a specific verb+resource combination. It is distinct from sibling tools like decode_base64 or html_to_text, so there is no confusion about its purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The implied usage is when a base64-encoded PDF needs to have its text extracted, but the description provides no explicit guidance on when to use this tool versus alternatives or any exclusions. It relies on the tool name and sibling context for differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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TDQS
A3.5/5.0
Disambiguation5/5
Each tool has a distinct purpose and target resource or operation. While some tools are thematically related (e.g., detect_secrets and classify_gdpr both analyze text), their specific outputs and use cases are clearly separated by names and descriptions.
Naming Consistency4/5
Most tools follow a clear verb_noun pattern (convert_currency, generate_uuid, validate_iban), and the noun_to_noun conversion tools (csv_to_json, html_to_text) form a consistent sub-pattern. The mix of verb_noun and X_to_Y is understandable and predictable, though not uniform.
Tool Count3/5
23 tools is on the higher end for a utility server, feeling like a grab-bag of many unrelated functions. While each tool is simple and serves a purpose, the count exceeds the typical well-scoped range, making it heavier than ideal.
Completeness3/5
The tool coverage is broad but scattered with no clear domain focus. Obvious complementary utilities are missing, such as URL encoding/decoding, YAML conversion, or PDF generation. However, within each small category, core operations are present, so agents can work around gaps.