Dumpling AI MCP Server

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by DumplingAI

Overview Schema Related Servers Score Discussions

JavaScript

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation3/5
Most tools have distinct purposes, but there is some overlap that could cause confusion. For example, 'extract' and 'scrape' both handle web data extraction, and 'search-places' and 'search-maps' both involve location searches, though their descriptions differ slightly. The AI-powered extraction tools (extract-audio, extract-document, etc.) are well-differentiated by media type.
Naming Consistency4/5
Tool names follow a consistent verb-noun pattern with hyphens, such as 'add-to-knowledge-base' and 'convert-to-pdf'. However, there are minor deviations like 'run-js-code' and 'run-python-code' using 'run' instead of more specific verbs, and 'get-autocomplete' could be clearer. Overall, the naming is predictable and readable.
Tool Count2/5
With 27 tools, the count is high and feels heavy for a single server, suggesting potential scope creep. While the tools cover diverse data processing tasks, a more focused set might improve usability. This many tools can overwhelm agents and increase the risk of misselection.
Completeness4/5
The server provides comprehensive coverage for data extraction, conversion, and search across various media types and sources. Minor gaps exist, such as no explicit tool for deleting or managing knowledge base entries, but core workflows like PDF handling (convert, merge, read/write metadata) and web interactions (crawl, search, screenshot) are well-covered.
Average 2.9/5 across 27 of 27 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions 'customizable parameters' but doesn't disclose critical behavioral traits such as rate limits, authentication needs, response format, or potential side effects. For an AI generation tool with no annotations, this leaves significant gaps in understanding how the tool behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that clearly states the tool's function. It's appropriately sized and front-loaded with the core purpose. There's no wasted verbiage, though it could benefit from additional context to improve completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of an AI text generation tool with no annotations and no output schema, the description is insufficient. It lacks details on response format, error handling, or how it integrates with sibling tools. The agent would struggle to use this effectively without more context about its behavior and outputs.
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?
Schema description coverage is 100%, with all parameters documented in the schema. The description adds no specific meaning beyond the schema's details about messages, agentId, parseJson, and threadId. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'Generate AI text completions with customizable parameters' which provides a clear verb ('Generate') and resource ('AI text completions'), but it doesn't distinguish from sibling tools like 'get-autocomplete' or 'search' which might also generate text. The purpose is understandable but lacks differentiation from similar tools in the server.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'get-autocomplete' or other AI-related tools. There's no mention of specific contexts, prerequisites, or exclusions. The agent must infer usage from the tool name and parameters alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 behavioral disclosure. It states 'Add' implies a write operation but doesn't cover permissions needed, rate limits, idempotency, or error handling. For a mutation tool with zero annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste, clearly front-loading the core action. Every word earns its place, making it easy to parse quickly without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity as a write operation with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits, return values, or error cases, leaving gaps that could hinder an AI agent's ability to use it correctly in context.
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?
Schema description coverage is 100%, so the schema already documents all three parameters (knowledgeBaseId, name, content) with basic descriptions. The description adds no additional meaning beyond what the schema provides, such as format examples or constraints, but doesn't contradict it either, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Add') and resource ('new text resources to a knowledge base'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'search-knowledge-base' or specify what types of text resources are supported beyond the generic term.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'search-knowledge-base' or other content-adding tools in the sibling list. It lacks context about prerequisites, such as needing an existing knowledge base, or exclusions, like not handling non-text resources.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 behavioral disclosure. It states the tool converts files to PDF but doesn't describe what happens during conversion (e.g., format limitations, error handling, or output characteristics). This is a significant gap for a tool with potential behavioral complexities like file processing.
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, efficient sentence: 'Convert various file formats to PDF.' It's front-loaded with the core purpose and has zero wasted words, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (conversion process) and lack of annotations or output schema, the description is incomplete. It doesn't address behavioral aspects like supported formats, conversion quality, or error cases. For a tool with no structured data to fall back on, this leaves significant gaps.
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 description coverage is 100%, with clear descriptions for both parameters. The description doesn't add any meaning beyond what the schema provides (e.g., it doesn't explain supported file formats or conversion details). Baseline is 3 since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Convert various file formats to PDF.' It specifies the action (convert) and resource (file formats to PDF). However, it doesn't distinguish from sibling tools like 'merge-pdfs' or 'write-pdf-metadata' which also handle PDFs, so it lacks sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context, or exclusions. For example, it doesn't clarify if this is for converting documents, images, or other formats, or how it differs from siblings like 'extract-document' or 'doc-to-text'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions recursive crawling and content extraction but omits critical details: whether it respects robots.txt, rate limits, authentication needs, error handling, or output structure. For a tool that performs web crawling (potentially resource-intensive and with legal implications), this is a significant gap.
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, efficient sentence with zero wasted words. It's front-loaded with the core functionality and uses precise language. Every word earns its place, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of web crawling (recursive traversal, potential side effects) and lack of annotations or output schema, the description is incomplete. It doesn't address behavioral aspects like politeness policies, data returned, or error conditions. For a tool with no structured safety hints and multiple parameters, more context is needed.
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?
Schema description coverage is 100%, so the schema fully documents all parameters (url, limit, depth, format). The description adds no additional parameter semantics beyond what's in the schema. The baseline score of 3 reflects adequate coverage by the schema alone, with no extra value from the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('recursively crawl') and resource ('websites') with an additional function ('extract content'). It distinguishes itself from sibling tools like 'scrape' or 'search' by emphasizing recursive traversal rather than single-page extraction. However, it doesn't explicitly differentiate from all siblings, such as 'extract-document' which might also extract content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'scrape' or 'extract'. It doesn't mention prerequisites, limitations, or typical use cases. The agent must infer usage from the tool name and parameters alone, which is insufficient for optimal selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 behavioral disclosure. It mentions conversion but doesn't specify supported formats, error handling, performance limits, or output details. For a tool with potential complexity in document processing, this lack of context is a significant gap, though it doesn't contradict any annotations.
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, efficient sentence that directly states the tool's purpose without any fluff. It's front-loaded and appropriately sized, making it easy to parse quickly, which is ideal for tool selection in an AI agent context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for a tool that handles document conversion. It doesn't cover behavioral aspects like format support or error cases, and with no output schema, it fails to explain what the plain text output entails. This leaves significant gaps for effective tool use.
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?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional meaning beyond implying conversion, which aligns with the schema but doesn't provide extra context like examples or constraints. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Convert') and resource ('various document formats to plain text'), making it immediately understandable. However, it doesn't explicitly distinguish this from sibling tools like 'extract-document' or 'read-pdf-metadata', which might have overlapping functionality, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'extract-document' and 'convert-to-pdf' available, there's no indication of scenarios where 'doc-to-text' is preferred, such as for text extraction from documents versus other extraction methods, leaving the agent to guess based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 behavioral disclosure. It mentions 'AI-powered instructions' but doesn't elaborate on how this affects behavior—such as potential latency, accuracy, rate limits, or authentication needs. For a tool that likely involves external API calls or processing, this lack of detail is a significant gap.
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, efficient sentence: 'Extract structured data from web pages using AI-powered instructions.' It's front-loaded with the core purpose and avoids unnecessary words, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (AI-powered extraction from web pages), lack of annotations, and no output schema, the description is insufficient. It doesn't cover behavioral aspects like error handling, output format, or limitations, leaving the agent with incomplete context for effective use.
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 input schema has 100% description coverage, clearly documenting both parameters ('url' and 'schema'). The description adds minimal value beyond this, as it doesn't explain the format or examples for the 'schema' parameter or provide additional context. With high schema coverage, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Extract structured data from web pages using AI-powered instructions.' It specifies the verb ('extract'), resource ('structured data from web pages'), and method ('AI-powered instructions'). However, it doesn't explicitly distinguish this tool from sibling tools like 'scrape' or 'crawl', which might have overlapping functionality, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'scrape' and 'crawl' that might handle similar web data extraction tasks, there's no indication of specific use cases, prerequisites, or exclusions. This leaves the agent without clear direction on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 behavioral disclosure. It states the tool extracts structured data based on a prompt, but doesn't describe what 'structured data' entails (e.g., JSON, text), potential limitations (e.g., audio quality requirements, processing time), or error handling. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
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, efficient sentence: 'Extract structured data from audio files based on a prompt.' It's front-loaded with the core action and resource, with no wasted words. Every part of the sentence contributes to understanding the tool's purpose, making it appropriately sized and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (audio processing with a prompt-driven extraction) and lack of annotations and output schema, the description is incomplete. It doesn't explain what 'structured data' means in the output, potential use cases, or limitations like supported audio formats or file sizes. For a tool with no structured data on behavior or output, more context is needed to guide effective use.
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 description adds minimal meaning beyond the input schema, which has 100% coverage with clear descriptions for all parameters. It implies that 'audio' refers to files and 'prompt' guides extraction, but doesn't elaborate on prompt examples or audio format specifics. With high schema coverage, the baseline is 3, as the schema does most of the work without needing extra detail in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Extract structured data from audio files based on a prompt.' It specifies the verb ('extract'), resource ('structured data'), and source ('audio files'), making it easy to understand. However, it doesn't explicitly differentiate from sibling tools like 'extract-video' or 'extract-document', which have similar extraction purposes but different input types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'extract-video' for video files or 'extract-document' for text extraction, nor does it specify prerequisites such as audio format compatibility or file size limits. Usage is implied by the tool name and description but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 behavioral disclosure. It mentions extraction based on a prompt, which implies AI/LLM processing, but doesn't disclose key traits: whether this is a read-only operation, potential costs or rate limits, error handling, or what happens with invalid inputs. For a tool with no annotations and complex behavior (AI-driven extraction), 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded with the core action ('extract structured data') and context ('from documents based on a prompt'), making it easy to parse. Every part of the sentence contributes essential information, with zero waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (AI-driven extraction from documents), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like permissions, rate limits, or error cases, and doesn't hint at the return format (though jsonMode parameter suggests JSON output). For a tool with 4 parameters and no structured safety hints, more context is needed to guide effective use.
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?
Schema description coverage is 100%, so the schema already documents all parameters (inputMethod, files, prompt, jsonMode) with basic descriptions. The description adds minimal value beyond the schema by implying that 'prompt' guides the extraction, but it doesn't explain parameter interactions (e.g., how inputMethod affects files format) or provide examples. Baseline 3 is appropriate as the schema does most of the work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'extract' and the resource 'structured data from documents', specifying the action and target. However, it doesn't differentiate from sibling tools like 'extract', 'extract-audio', 'extract-image', or 'extract-video', which all perform extraction but on different content types. The description is specific about what gets extracted (structured data) but doesn't clarify the distinction from other extraction tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when this extraction method is appropriate compared to other extraction tools (e.g., 'extract-audio' for audio, 'extract-image' for images) or general tools like 'extract'. There's no context about prerequisites, such as document formats supported, or exclusions, leaving the agent to guess based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the action 'extract structured data' but doesn't disclose behavioral traits like rate limits, authentication needs, error handling, or what 'structured data' entails (e.g., format, size limits). This is a significant gap for a tool with no annotation coverage.
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, efficient sentence with zero waste. It's front-loaded with the core purpose and appropriately sized for the tool's complexity, earning its place without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It doesn't explain the return values (what 'structured data' means), error cases, or operational constraints. For a tool with 4 parameters and no structured behavioral hints, more context is needed to be fully helpful.
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?
Schema description coverage is 100%, so the schema documents all parameters. The description adds no meaning beyond the schema—it doesn't explain how 'prompt' guides extraction or what 'structured data' looks like. Baseline 3 is appropriate as the schema handles parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'extract' and the resource 'structured data from images', with the qualifier 'based on a prompt' adding specificity. It distinguishes from siblings like 'extract-audio' or 'extract-document' by focusing on images, though it doesn't explicitly contrast with 'extract' (which might handle other data types).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites, limitations, or compare it to other extraction tools (e.g., 'extract-document' for text from documents). The description implies usage for image-based extraction but lacks explicit context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'extract structured data' but doesn't disclose behavioral traits such as processing time, rate limits, authentication needs, error handling, or what 'structured data' entails (e.g., JSON, text). This leaves gaps in understanding how the tool behaves beyond its basic function.
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, efficient sentence that front-loads the core purpose without unnecessary words. Every part ('extract structured data', 'from videos', 'based on a prompt') contributes directly to understanding the tool's function, making it appropriately sized and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of video data extraction with no annotations and no output schema, the description is incomplete. It doesn't address key contextual aspects like output format (beyond 'structured data'), limitations (e.g., video length, supported codecs), or integration with sibling tools. For a tool with 4 parameters and no structured behavioral hints, more detail is needed to be fully helpful.
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?
Schema description coverage is 100%, so the schema already documents all parameters. The description adds no additional meaning beyond implying that 'prompt' guides the extraction, but doesn't clarify prompt format, examples, or constraints. With high schema coverage, the baseline is 3, and the description doesn't significantly enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('extract structured data') and resource ('from videos'), specifying the mechanism ('based on a prompt'). It distinguishes from siblings like 'extract-audio' or 'extract-document' by focusing on video content, but doesn't explicitly contrast with 'extract' or 'extract-image' which might overlap in data extraction contexts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., video format support), exclusions (e.g., not for real-time processing), or direct comparisons to sibling tools like 'extract' or 'extract-image' that might handle similar tasks. The description only states what it does, not when it's appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 behavioral disclosure. 'Generate AI images from text prompts' indicates a creation operation but doesn't disclose traits like rate limits, authentication needs, cost implications, or output behavior (e.g., whether images are saved or returned as data). For a generative AI tool with zero annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Generate AI images from text prompts.' It is front-loaded with the core purpose and contains no wasted words, making it highly concise and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of an AI image generation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., latency, costs), output format (e.g., image URLs or binary data), and error handling. For a tool with 2 parameters (including a nested object) and rich functionality, the description should provide more context to be fully helpful.
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?
Schema description coverage is 100%, so the schema already documents all parameters (e.g., 'model', 'input.prompt'). The description adds no additional meaning beyond what the schema provides—it doesn't explain parameter relationships, default values, or usage examples. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate AI images from text prompts.' It specifies the verb ('Generate') and resource ('AI images'), and distinguishes it from sibling tools like 'extract-image' or 'screenshot' which handle existing images rather than generating new ones. However, it doesn't explicitly differentiate from all siblings (e.g., 'generate-agent-completion' is also a generation tool but for text).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a text prompt), exclusions (e.g., not for editing existing images), or comparisons to sibling tools like 'extract-image' for image extraction or 'generate-agent-completion' for text generation. Usage is implied only by the tool's name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('Retrieve') but doesn't describe what the tool returns (e.g., list of suggestions, format), any rate limits, authentication needs, or error conditions. This leaves significant gaps in understanding how the tool behaves beyond its basic purpose.
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, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to understand at a glance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., the format of autocomplete suggestions), any limitations or dependencies, or how it differs from related tools. For a tool with four parameters and no structured output information, this leaves too much unspecified.
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 description coverage is 100%, so the schema already documents all four parameters (query, location, country, language) with descriptions. The description doesn't add any meaning beyond what's in the schema, such as explaining how these parameters affect the autocomplete results or providing examples beyond the schema's basic descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Retrieve') and resource ('Google autocomplete suggestions') with the purpose ('for a query'). It's specific about what the tool does, though it doesn't explicitly differentiate from sibling tools like 'search' or 'search-places' which might also retrieve information from Google services.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'search' or 'search-places' that might offer similar or overlapping functionality, nor does it specify contexts where autocomplete suggestions are preferred over full search results.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 behavioral disclosure. While 'retrieve' implies a read operation, the description doesn't mention important behavioral aspects like rate limits, authentication requirements, data freshness, pagination, or error conditions. This leaves significant gaps for an agent to understand how to use this tool effectively.
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, clear sentence that efficiently communicates the core purpose without unnecessary words. It's appropriately sized for a tool with good schema documentation and gets straight to the point with zero wasted text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 7 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns (review format, fields included), doesn't mention authentication or rate limiting considerations, and provides no guidance on parameter combinations or error handling. The schema handles parameter documentation, but the description fails to address other critical contextual aspects.
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 description provides no parameter-specific information beyond what's already in the schema, which has 100% coverage with detailed descriptions for all 7 parameters. The baseline score of 3 reflects that the schema adequately documents parameters, but the description adds no additional semantic context about how parameters interact or which are most important.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('retrieve') and resource ('Google reviews for businesses or places'), making the purpose immediately understandable. However, it doesn't differentiate this tool from potential sibling tools like 'search-places' or 'search-maps' that might also return review-related information, preventing a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'search-places' and 'search-maps' available, there's no indication whether this tool is specialized for reviews only or how it differs from broader search tools that might include reviews.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions 'optional parameters for timestamps and language preferences,' it doesn't describe what happens when transcripts aren't available, rate limits, authentication needs, error conditions, or the format/structure of the returned transcript. For a tool that interacts with external services, this leaves significant behavioral gaps.
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 efficiently communicates the core functionality and key optional features. Every word earns its place with no redundancy or fluff. It's appropriately sized for a tool with clear parameters documented elsewhere.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of extracting transcripts from an external service like YouTube, the description is insufficient. With no annotations and no output schema, it doesn't address critical aspects like error handling (e.g., if the video has no transcript), rate limits, authentication requirements, or the structure of the returned data. The agent lacks necessary context for reliable tool invocation.
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?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value by mentioning 'optional parameters for timestamps and language preferences,' which loosely references 'includeTimestamps' and 'preferredLanguage' but doesn't provide additional context beyond what's in the schema. This meets the baseline of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Extract transcripts') and resource ('from YouTube videos'), making the purpose immediately understandable. It distinguishes itself from sibling tools like 'extract-audio' or 'extract-video' by focusing specifically on transcripts. However, it doesn't explicitly differentiate from potential transcript-related tools that might exist elsewhere, keeping it at a 4 rather than a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites (like needing a valid YouTube URL), nor does it compare with sibling tools like 'extract-video' or 'extract-audio' that might handle different aspects of YouTube content. The agent must infer usage context solely from the tool name and parameters.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the basic function but fails to mention important behavioral aspects like whether this is a read-only or destructive operation, what permissions are required, how errors are handled, or what the output looks like (e.g., file format, size limits).
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, clear sentence that efficiently communicates the core function without any unnecessary words. It's perfectly front-loaded and wastes no space.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 6 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the merged PDF output looks like, doesn't mention any constraints or limitations, and provides minimal context for a moderately complex PDF manipulation tool.
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 description coverage is 100%, so all parameters are documented in the schema. The description adds no additional semantic information about parameters beyond what's already in the schema, meeting the baseline expectation but not providing extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'Combine multiple PDF files into a single document' with a specific verb (combine) and resource (PDF files). However, it doesn't explicitly differentiate from sibling tools like 'convert-to-pdf' or 'write-pdf-metadata', which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'convert-to-pdf' or 'write-pdf-metadata'. It lacks any context about prerequisites, limitations, or comparative use cases with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't describe how it behaves—e.g., error handling, rate limits, authentication needs, or what metadata is extracted. This leaves significant gaps for a tool that processes files.
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, efficient sentence that front-loads the core purpose without waste. It's appropriately sized for a simple tool, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of handling PDF files and the lack of annotations and output schema, the description is incomplete. It doesn't explain what metadata is returned, error conditions, or operational constraints, which are crucial for effective use.
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?
Schema description coverage is 100%, so the schema already documents all parameters. The description doesn't add any meaning beyond the schema, such as explaining the relationship between 'inputMethod' and 'files' or what 'requestSource' is for. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Extract' and the resource 'metadata from PDF files', making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'extract-document' or 'extract-image', which could also involve PDF processing, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'extract-document' or 'write-pdf-metadata'. It lacks context about prerequisites, limitations, or specific use cases, offering minimal usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'sandbox' which implies isolation and safety, but fails to detail critical aspects like execution limits, error handling, output format, or security constraints. This is inadequate for a code execution tool.
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, efficient sentence with zero wasted words. It is front-loaded with the core action and resource, making it immediately understandable without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex tool like code execution with 5 parameters and no output schema, the description is insufficient. It lacks details on return values, error conditions, sandbox limitations, and integration with sibling tools, leaving significant gaps in understanding.
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?
Schema description coverage is 100%, so the schema fully documents all parameters. The description adds no additional semantic context beyond what's in the schema, such as examples or usage notes. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Execute') and resource ('JavaScript code in a sandbox'), making the purpose unambiguous. However, it doesn't explicitly differentiate from its sibling 'run-python-code' beyond the language specification, which is implied but not stated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'run-python-code' or other execution-related tools. It lacks context about appropriate use cases, prerequisites, or exclusions, leaving the agent with minimal direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'sandbox' which hints at isolation, but doesn't specify execution limits, error handling, output format, or security implications. For a code execution tool with zero annotation coverage, this leaves significant behavioral gaps.
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, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded with the core purpose and avoids any redundant information, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of executing arbitrary Python code and the lack of annotations and output schema, the description is insufficient. It doesn't cover critical aspects like execution environment details, safety guarantees, error responses, or output handling, leaving the agent with incomplete context for safe and effective use.
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?
Schema description coverage is 100%, providing clear documentation for all 6 parameters. The description doesn't add any parameter-specific context beyond what's in the schema, such as examples or constraints. With high schema coverage, the baseline score of 3 is appropriate as the schema handles the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Execute') and resource ('Python code in a sandbox'), making the purpose immediately understandable. It distinguishes from sibling 'run-js-code' by specifying Python, though it doesn't explicitly contrast with other execution or processing tools like 'extract' or 'generate-agent-completion'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives is provided. The description doesn't mention when to choose this over 'run-js-code' for scripting, or when to prefer other processing tools like 'extract' or 'generate-agent-completion' for specific tasks. Usage is implied but not articulated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure but only states what the tool does at a high level. It doesn't mention important behavioral traits like rate limits, authentication requirements, potential for blocking by websites, error handling, or what 'cleaned' content entails. For a web scraping tool with zero annotation coverage, this leaves significant gaps in understanding its operational characteristics.
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, efficient sentence that gets straight to the point with zero wasted words. It's appropriately sized for a tool with clear parameters in the schema and is front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of web scraping (involving network operations, potential failures, and format transformations) with no annotations and no output schema, the description is insufficient. It doesn't address what the tool returns, error conditions, performance characteristics, or limitations (e.g., cannot scrape authenticated pages). For a 4-parameter tool with behavioral implications, more contextual information would be needed.
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 description adds no parameter-specific information beyond what's already in the schema (which has 100% coverage). It doesn't explain what 'cleaned' means in practice, when to choose different formats, or the implications of JavaScript rendering. Since schema coverage is high, the baseline score of 3 is appropriate, but the description doesn't compensate with additional semantic context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('extract and parse') and resource ('content from any web page'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'crawl', 'screenshot', or 'extract-document', which might have overlapping functionality for web content extraction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'crawl' (for multiple pages), 'screenshot' (for visual capture), or 'extract-document' (for document files). It lacks any context about appropriate use cases, prerequisites, or exclusions, leaving the agent to infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 behavioral disclosure. It mentions 'customizable settings' but doesn't describe key behaviors: whether this is a read-only operation, what happens on errors (e.g., invalid URLs), performance characteristics (e.g., timeouts), or output format (e.g., image bytes, file path). For a tool with 12 parameters and no annotation coverage, this is insufficient.
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, efficient sentence: 'Capture screenshots of web pages with customizable settings.' It's front-loaded with the core purpose and wastes no words. Every part earns its place by conveying essential information concisely.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (12 parameters, nested objects) and lack of annotations and output schema, the description is incomplete. It doesn't address behavioral aspects (e.g., what the tool returns, error handling), usage context, or how parameters work together. For a sophisticated screenshot tool, this leaves significant gaps for an AI agent.
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?
Schema description coverage is 100%, so the schema already documents all 12 parameters thoroughly. The description adds minimal value beyond the schema by hinting at 'customizable settings', but doesn't explain parameter interactions (e.g., how 'clipRectangle' relates to 'fullPage') or provide usage examples. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Capture screenshots of web pages with customizable settings.' It specifies the verb ('capture'), resource ('screenshots of web pages'), and scope ('customizable settings'). However, it doesn't explicitly differentiate from sibling tools like 'scrape' or 'extract-image', which might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., internet access), exclusions (e.g., not for non-web content), or compare it to siblings like 'scrape' (which might extract text) or 'extract-image' (which might extract existing images).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. While 'Perform Google web searches' implies a read-only operation that queries external data, the description doesn't address important behavioral aspects like rate limits, authentication requirements, potential costs, error conditions, or what the output looks like. The mention of 'customizable parameters' hints at flexibility but doesn't describe the tool's actual behavior beyond the basic operation.
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 extremely concise at just one sentence: 'Perform Google web searches with customizable parameters.' It's front-loaded with the core purpose and wastes no words. Every element of the sentence contributes meaning - the action, target, and key capability are all efficiently communicated.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (9 parameters including nested objects), absence of annotations, and lack of output schema, the description is insufficiently complete. A search tool with scraping capabilities, location filtering, and date ranges needs more context about what results to expect, how scraping works, limitations, and how this differs from other search tools on the server. The single-sentence description doesn't provide enough information for an agent to understand the tool's full capabilities and constraints.
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?
Schema description coverage is 100%, meaning all parameters are documented in the schema itself. The description adds minimal value beyond what's in the schema - it mentions 'customizable parameters' but doesn't provide additional context about how parameters interact or which combinations are most useful. With complete schema documentation, the baseline score of 3 is appropriate as the description doesn't significantly enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Perform Google web searches with customizable parameters.' It specifies the verb ('perform'), resource ('Google web searches'), and scope ('with customizable parameters'). However, it doesn't explicitly distinguish this from sibling tools like 'search-knowledge-base', 'search-maps', 'search-news', or 'search-places', which all perform different types of searches.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With multiple sibling search tools (search-knowledge-base, search-maps, search-news, search-places), there's no indication that this is specifically for general web searches versus those specialized alternatives. The description mentions 'customizable parameters' but doesn't explain when those parameters are needed or what scenarios this tool is best suited for.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 behavioral disclosure. It states the tool searches for 'relevant information' but doesn't explain what 'relevant' means (e.g., ranking, filtering, or relevance algorithms), whether it's read-only (implied but not explicit), or any limitations like rate limits or authentication needs. This leaves significant gaps for an agent to understand how the tool behaves beyond basic functionality.
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, efficient sentence with no wasted words: 'Search a knowledge base for relevant information.' It's front-loaded and appropriately sized for the tool's complexity, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a search tool with no annotations and no output schema, the description is incomplete. It doesn't cover behavioral aspects (e.g., how results are returned, error handling) or usage context, leaving the agent with insufficient information to fully understand the tool's operation and limitations in this server environment.
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 description coverage is 100%, meaning all parameters are documented in the schema itself. The description doesn't add any additional meaning beyond what's in the schema (e.g., it doesn't explain how the query is processed or what the knowledgeBaseId refers to). With high schema coverage, the baseline score is 3, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'Search a knowledge base for relevant information,' which includes a specific verb ('search') and resource ('knowledge base'). It distinguishes from some siblings like 'add-to-knowledge-base' (which creates content) but doesn't explicitly differentiate from other search tools like 'search' or 'search-news' that might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to prefer this over other search tools (e.g., 'search', 'search-news') or when it's appropriate given the context of knowledge bases. There's no information about prerequisites, such as needing an existing knowledge base or specific access rights.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 behavioral disclosure. While 'search' implies a read-only operation, the description doesn't address important behavioral aspects like rate limits, authentication requirements, result format, pagination behavior, or whether this is a real-time search versus cached data. For a search tool with 6 parameters and no annotation coverage, this is inadequate.
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 extremely concise - a single sentence that gets straight to the point. There's no wasted language or unnecessary elaboration. It's front-loaded with the core functionality and doesn't bury important information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with 6 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what kind of results to expect, how results are structured, whether there are limitations or quotas, or how to interpret the various search parameters. The agent would need to guess about important operational aspects.
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?
Schema description coverage is 100%, so the schema already documents all 6 parameters thoroughly. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it doesn't explain relationships between parameters (e.g., how 'gpsPositionZoom' affects results) or provide usage examples. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search Google Maps for locations and businesses.' It specifies the verb ('search'), resource ('Google Maps'), and target ('locations and businesses'). However, it doesn't differentiate from sibling tools like 'search-places' or 'search', which might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'search-places' and 'search' available, there's no indication of what makes this tool distinct or when it should be preferred over those options. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 behavioral disclosure. It mentions 'search' but doesn't describe what the search returns (e.g., article list, summaries), pagination behavior, rate limits, authentication needs, or error handling. For a search tool with 6 parameters and no annotations, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose without any wasted words. It's appropriately sized for a search tool, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (6 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the return values, result format, or behavioral aspects like pagination or source limitations. For a search tool with multiple filtering options, more context is needed to guide effective use.
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?
Schema description coverage is 100%, meaning all parameters are documented in the schema itself. The description adds no additional meaning beyond what's in the schema (e.g., it doesn't explain how 'country' and 'location' interact or provide examples beyond the schema's 'e.g.' notes). Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('search') and resource ('news articles across multiple sources'), making it immediately understandable. However, it doesn't distinguish this tool from the sibling 'search' tool, which appears to be a more general search function, so it misses full differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With a sibling tool named 'search' that likely serves a broader purpose, there's no indication of when to prefer news-specific searching over general searching or other search-related tools like 'search-knowledge-base' or 'search-maps'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 behavioral disclosure. While 'search' implies a read-only operation, the description doesn't address important behavioral aspects like rate limits, authentication requirements, result format, pagination behavior (despite having a 'page' parameter), or what 'detailed business information' specifically includes.
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 extremely concise at just one sentence with no wasted words. It's front-loaded with the core purpose and efficiently communicates the essential function. Every word earns its place in this minimal description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with 5 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't address what the tool returns, how results are structured, whether there are limitations or constraints, or how it differs from similar search tools on the server. The agent would need to guess about important operational aspects.
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?
With 100% schema description coverage, the input schema already documents all 5 parameters adequately. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain relationships between parameters (e.g., how 'country' and 'location' interact), provide examples of effective queries, or clarify what 'detailed business information' means in the context of these parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'Search for places with detailed business information,' which specifies the verb (search), resource (places), and type of information returned (detailed business information). However, it doesn't differentiate from sibling tools like 'search-maps' or 'search,' leaving some ambiguity about when to use this specific place search tool versus other search tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'search-maps' and 'search' available, there's no indication of what makes this place search tool distinct or when it should be preferred over other search capabilities on the server.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'trims videos' but doesn't reveal critical behaviors: whether this is a destructive operation (overwrites original), requires specific permissions, has rate limits, or what the output looks like (e.g., returns a new video URL). For a mutation tool with zero annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (5 words) and front-loaded, stating the core purpose immediately with zero wasted words. Every element earns its place, making it easy for an agent to parse quickly while scanning tool lists.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (video processing mutation), lack of annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like side effects, output format, error conditions, or usage context. For a tool that modifies media, this leaves the agent with insufficient information to use it correctly and safely.
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?
Schema description coverage is 100%, with all three parameters clearly documented in the schema. The description adds no additional parameter semantics beyond implying duration trimming via start/end timestamps. This meets the baseline score of 3 since the schema does the heavy lifting, but the description doesn't enhance understanding of parameter usage or constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('trim') and resource ('videos'), and specifies the action ('to a specific duration'). It distinguishes itself from sibling tools like 'extract-video' or 'extract-audio' by focusing on duration modification rather than extraction. However, it doesn't explicitly differentiate from all siblings, leaving some ambiguity about when to choose this over other video-related tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., video format compatibility), when not to use it, or how it compares to sibling tools like 'extract-video'. The agent must infer usage from the purpose alone, which is insufficient for optimal tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. 'Write metadata' implies a mutation operation, but it doesn't disclose whether this overwrites existing metadata, requires specific permissions, handles errors, or has side effects. The description lacks critical behavioral context for a write operation.
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, efficient sentence with zero wasted words. It's front-loaded with the core action and resource, making it immediately scannable and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description is inadequate. It doesn't explain what happens after writing metadata (e.g., success/failure responses, file modifications), nor does it cover behavioral aspects like error handling or limitations. The context demands more completeness for safe agent use.
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?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional meaning about parameters beyond what's in the schema, such as explaining metadata field formats or file handling. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Write') and resource ('metadata to PDF files'), making the purpose immediately understandable. It doesn't distinguish from sibling tools like 'read-pdf-metadata' or 'merge-pdfs', but the action is specific enough to understand what it does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'read-pdf-metadata' or 'merge-pdfs'. There's no mention of prerequisites, limitations, or typical use cases, leaving the agent to infer usage from context alone.
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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