pdf_metadata
PDF metadata without full extract
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | PDF URL |
PDF metadata without full extract
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | PDF URL |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / argsRemoved value: -{
- "description": "Tool arguments",
- "properties": {
- "text": {
- "description": "Primary input text",
- "type": "string"
- }
- },
- "type": "object"
-}Input schema / properties / urlAdded value: +{
+ "description": "PDF URL",
+ "type": "string"
+}Input schema / requiredPrevious value: -[]New value: +[
+ "url"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries full responsibility for behavioral disclosure. It only says 'PDF metadata without full extract', which does not detail what metadata is returned, whether the operation is read-only, or any error/limit behavior. 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 extremely brief, using only five words, and is front-loaded with the key purpose. It is concise without unnecessary filler, though the terseness might sacrifice some clarity.
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?
With no output schema, no annotations, and a very short description, the tool lacks enough context to understand what metadata will be returned or any side effects. The single parameter is documented, but the description does not compensate for the missing output information, leaving the tool incomplete for effective use.
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 fully describes the single parameter 'url' with 'PDF URL', providing 100% schema coverage. The description does not add additional meaning to the parameter, such as accepted formats or validation rules, but since the schema is sufficient, a baseline score of 3 is appropriate.
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 'PDF metadata without full extract' clearly indicates the tool retrieves metadata from a PDF, distinguishing it from the sibling tool 'extract_pdf' that likely performs full extraction. However, it lacks an explicit verb like 'extract' or 'get', making it slightly less direct.
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 phrase 'without full extract' implies the tool is suitable when only metadata is needed, but it does not explicitly state when to use it instead of alternatives such as 'extract_pdf'. No exclusions or alternative tool names are mentioned.
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.
Multiple tools have overlapping or identical purposes, such as ocr_url and ocr_image (both OCR from an image URL), compare_texts and text_diff (both compare or diff texts), extract_url and read_url (both extract webpage content), and content_hash and hash_text (both compute hashes). The boundaries between these tools are unclear, causing a high risk of misselection.
Naming conventions are mixed. Many tools use verb_noun (extract_url, validate_email), but others use noun_verb (language_detect, html_clean), single words (advisor, crawl, retrieve), or noun_noun (job_status, page_metadata). This inconsistency makes it harder to predict tool names.
With 100 tools, the server is extremely over-scoped for a generic agent toolkit. While some tools are distinct and useful, the sheer number does not align with a focused purpose; many tools are redundant or highly specialized, and the count exceeds what is typically manageable for an agent to reason about.
The toolkit covers a broad range of utilities including extraction, validation, processing, research, memory, and orchestration. However, there are no CRUD tools for creating/updating/deleting resources, no database or file system operations, and no integration beyond web/API basics. This leaves significant gaps for agents that need general lifecycle management, though it does handle many common tasks.