Engineering Documentation MCP Server

by mfaizanmumtaz

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools target distinct outputs (README, architecture, API, database, exports), but generate_project_documentation is a superset that overlaps with the individual generation and export tools, creating mild ambiguity about when to use it versus the separate tools.
Naming Consistency5/5
All tool names follow a consistent verb_noun snake_case pattern (analyze_repository, generate_readme, export_pdf, share_google_doc), making the naming predictable and easy to navigate.
Tool Count5/5
With 12 tools, the server is well-scoped for an engineering documentation workflow, covering analysis, generation, publishing, and exporting without excessive redundancy.
Completeness4/5
The tool surface covers the core documentation lifecycle (analyze, generate, publish, export, share), but lacks simple management operations like listing or deleting Google Docs, which are minor gaps for a full workflow.
Average 3.6/5 across 12 of 12 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 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.
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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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals the output format (HTML, Mermaid) but omits side effects such as file writing, overwrite behavior, or dependencies. This leaves the agent uncertain about what invoking the tool will do.
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 concise sentence that is front-loaded with the primary action and output format. Every word earns its place with no unnecessary filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the full schema coverage and presence of an output schema, the description is minimally adequate. However, it lacks usage context and behavioral detail, making it incomplete for an agent deciding when to use this 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 input schema covers all three parameters (repo_path, title, output_path) with complete descriptions. The description adds no additional parameter semantics, so the baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates documentation and exports it as a styled HTML file with Mermaid support. This distinguishes it from sibling tools like export_markdown and export_pdf by specifying the output format and key feature.
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 offers no guidance on when to choose this tool over alternatives like export_markdown or export_pdf. It does not mention use cases, exclusions, or compare with siblings.
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, and the description only restates the core operation. It does not disclose important behavioral details such as whether it writes to the filesystem, overwrites existing files, requires network access, or what happens when output_path is not provided.
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, front-loaded sentence with no redundant words. It succinctly communicates the tool's primary purpose and output.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With full schema coverage and an output schema present, the basic mechanics are adequately covered. However, the description is thin on usage context, especially relative to sibling tools, and leaves behavioral details such as file-handling behavior 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?
All three parameters are fully described in the schema (100% coverage), so the description adds little beyond reinforcing that repo_path refers to a repository and output_path refers to a Markdown file. This meets the baseline but does not exceed it.
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 (generate documentation) and the output format (Markdown file), which distinguishes it from HTML/PDF export siblings. However, it does not explicitly differentiate it from generate_project_documentation, which may 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for when a user wants repository documentation as a Markdown file, but it offers no explicit guidance on when to choose this tool over closely related siblings like generate_project_documentation or export_html/export_pdf.
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 that the tool returns Mermaid syntax diagrams, but it does not clarify whether it reads from the repository only, writes files, or has side effects. The word 'Generate' could imply writing, though 'Returns' suggests a pure return value.
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 two concise sentences, front-loaded with the core action and immediately specifying the output format. Every word earns its place with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the tool's purpose and output, and an output schema exists. However, it lacks usage context, operational limitations, and guidance on how this tool fits among siblings. Given the moderate complexity of generating architecture diagrams, this is adequate but has clear 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?
Schema coverage is 100% for the single repo_path parameter, and the description merely refers to 'repository' without adding additional meaning beyond what the schema already provides. 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Generate') and resource ('architecture documentation for a repository'), and clearly defines the output ('system architecture flowchart and ER diagram in Mermaid syntax'). This distinguishes it from sibling tools like generate_project_documentation, which likely produces broader documentation.
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 gives no guidance on when to use this tool versus alternatives such as analyze_repository or generate_project_documentation. There is no mention of prerequisites, exclusions, or preferred use cases.
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 'regenerate' and 'update' but does not reveal whether existing content is overwritten, whether permissions are needed, or any side effects. This lack of detail is a significant gap for a mutation 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 sentence, compact and to the point. It front-loads the action and resource, with no wasted words. Every word contributes to the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The presence of an output schema and full parameter descriptions reduces the need to explain return values and parameters. However, the description lacks usage guidance and behavioral nuance about the regeneration process, making it adequate for simple selection but not fully complete for all invocation contexts.
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 baseline is 3. The tool description adds little beyond the schema; 'by ID' repeats the doc_id description, and 'regenerate documentation' is implicit in repo_path. No additional parameter meaning is provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Regenerate documentation and update an existing Google Doc by ID.' It uses a specific verb ('update') and resource ('existing Google Doc'), and distinguishes itself from siblings like create_google_doc (create vs update) and share_google_doc.
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 is provided on when to use this tool versus alternatives. The description implies it is for updating existing docs, but does not state exclusions, prerequisites, or mention alternatives such as create_google_doc for new documents.
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 must disclose behavioral traits. It mentions 'based on code analysis' but does not reveal whether the tool overwrites an existing README, requires special permissions, or performs side effects like writing files. The output_path parameter hints at file creation, but the description itself omits this critical behavioral detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one sentence that is front-loaded with the core action and resource, followed by a compact list of included sections. Every word earns its place, with no redundancy or unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter tool with an output schema, the description covers the main purpose and output contents. However, it lacks usage guidelines and behavioral transparency (e.g., file overwriting, analysis scope), which are needed for an agent to select and invoke it confidently among sibling documentation tools.
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% for both parameters (repo_path and output_path), so the schema already documents their meaning. The description adds no additional parameter semantics beyond mentioning the output content sections. Per the rubric baseline for high schema coverage, a score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Generate a professional README.md for a repository based on code analysis.' It specifies the resource (README.md), the input (repository), and the method (code analysis). This distinguishes it from sibling tools like generate_project_documentation (broader), document_api, and generate_architecture, which target different artifacts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for generating READMEs from code analysis but does not explicitly state when to use this tool over alternatives like generate_project_documentation or document_api. There are no exclusions or alternative naming, so the agent must infer context from the sibling list and purpose.
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 bears full responsibility for disclosing behavior. It only states the basic action and target, but omits any details about access permission handling, potential overwriting of existing permissions, notification side effects, or required authentication scopes. This is a significant gap for a mutation 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, tight sentence of 11 words. Every word contributes to conveying the core purpose, and it is front-loaded with the action. There is no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has a moderate complexity with three parameters, but the schema fully documents them and an output schema exists (per context signals). The description covers the primary purpose but leaves gaps about behavioral nuances, use-case boundaries, and permission implications. It is minimally viable but has clear 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 input schema provides 100% coverage with descriptions for all three parameters, so the baseline is 3. The description adds a little context by clarifying the email parameter ('one or more email addresses') and the doc_id ('existing Google Doc'), but it does not add substantive meaning beyond what the schema already documents.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (share), the resource (existing Google Doc), and the target (one or more email addresses). It distinguishes itself from sibling tools like create_google_doc and update_google_doc by specifying 'existing' and focusing on sharing rather than creation or modification.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'existing Google Doc' implies that this tool is for documents already created, giving some usage context. However, it provides no explicit guidance on when to use this tool versus alternatives, nor does it mention any exclusions or prerequisites. The usage is implied rather than clearly directed.
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 discloses that the tool creates a document (a mutation), but it omits critical behavioral details like required authentication, whether it overwrites an existing doc with the same title, potential side effects on Google Drive, or error conditions. This is insufficient transparency for a creation 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 concise sentence that clearly communicates the core purpose without any fluff. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has moderate complexity with an output schema and related sibling tools. The description is minimal but leaves ambiguity: it says 'previously generated documentation content' yet the schema mentions repo_path 'will be analyzed on-the-fly', implying possible contradiction about the source. It also lacks explicit prerequisite context (e.g., run generate_project_documentation first). This is incomplete but not severely so.
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 about parameters, such as how repo_path is used or what share_with does. Baseline of 3 is appropriate since the schema carries the load.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool creates a formatted Google Doc from previously generated content, using a specific verb and resource. It distinguishes itself from sibling tools like generate_project_documentation (which generates content) and update_google_doc (which updates an existing doc).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool: after documentation has been generated, as indicated by 'from previously generated documentation content'. However, it does not explicitly mention alternatives or exclusions, such as using update_google_doc for edits or export_markdown for non-Google formats.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does disclose a critical prerequisite (weasyprint and libpango/libcairo), which is useful context. However, it does not describe side effects, whether it overwrites existing output files, or any error conditions beyond missing dependencies.
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 two short sentences, front-loaded with the core purpose and followed by a key dependency note. Every sentence earns its place, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (3 params, no annotations), the description covers its main purpose and a key prerequisite. However, there is ambiguity about whether it generates documentation from scratch or exports pre-existing documentation, especially with sibling tools like generate_project_documentation. The presence of an output schema may compensate, but the description alone leaves this gap.
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 already provides descriptions for all three parameters (title, repo_path, output_path) with 100% coverage. The description does not add any additional parameter-specific meaning, such as default behavior for output_path when null, or how title is used in the document. Since schema coverage is high, a baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates documentation and exports it as a PDF file, using a specific verb ('Generate') and resource ('documentation') with a target format. This distinguishes it from sibling tools like export_markdown and export_html, which target other formats, and generate_project_documentation, which likely only generates documentation without exporting.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when PDF output is needed, but does not explicitly state when to use this tool over alternatives like export_markdown or export_html, nor does it mention exclusions or prerequisites beyond the weasyprint/system dependencies. It provides some context by noting the required dependencies, but lacks clear when-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the output format ('Returns tables with fields, types, and relationships'), which is helpful, but it does not state whether the tool modifies the repository, requires specific permissions, or how it handles repositories without supported ORMs. This is adequate but not rich.
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 two sentences, front-loaded with the action and scope. The list of ORMs is compact and informative, with no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter) and existing output schema info (though not shown), the description adequately explains what the tool returns and its supported ORMs. It lacks details on edge cases or fallback behavior, but overall it is reasonably complete for its complexity.
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% coverage for the single parameter repo_path, describing it as 'Path to the repository.' The description adds no additional parameter-level detail, so it meets the baseline without contribution.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses specific verbs ('Extract and document') and identifies the resource ('all database models from a repository'). It lists supported ORMs to further clarify scope, distinguishing it from siblings like document_api or generate_project_documentation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies its usage by naming the target resource (database models) and supported ORM frameworks, but it does not explicitly state when to use it over alternatives or mention any exclusions. This is clear context but lacks explicit guidance on when not to use or what sibling tools to prefer.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. It clearly states the tool analyzes and returns metadata, but it does not explicitly state that it is read-only, nor does it mention behavior for invalid paths or potential performance implications. Core behavior is covered, but edge cases are not.
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, front-loaded sentence that efficiently conveys the purpose and output. The list of metadata categories is informative without being redundant.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity of one parameter and the presence of an output schema, the description adequately covers what the tool does and what it returns. It provides a clear scope of the analysis, making it sufficient for an agent to correctly select and invoke the 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?
Schema description coverage is 100% for the sole parameter repo_path, which is fully described as an absolute or relative path. The tool description adds no additional nuance about parameters, so the baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool analyzes a software repository and returns detailed metadata. It lists specific metadata categories (languages, frameworks, API endpoints, etc.), distinguishing it from sibling tools that generate documentation rather than analyze and report.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when repository metadata is needed, but it does not explicitly mention when to avoid using it or name any alternative tools. Sibling tools like generate_project_documentation could overlap, but no exclusions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 does disclose that the tool analyzes code, publishes to Google Docs, exports files, and returns URLs/paths, but it omits side effects, prerequisites (e.g., authentication), and conditional behavior such as interactions between publish_to_google_docs and share_with.
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 two sentences, action-first, with a clear colon-separated list of steps and the return value. Every clause contributes to understanding the tool's role, making it concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex tool with seven parameters and an output schema, the description captures the primary workflow, optional exports, and return values. It could improve by suggesting when to choose individual sibling tools, but the current level is adequate for selection and 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 each parameter. The description only reiterates 'optionally export to Markdown/HTML/PDF' and adds no new meaning beyond what the input schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description immediately states the tool's core action with 'Run the full documentation pipeline on a repository' and enumerates each stage (analyze, generate, publish, optionally export). This clearly distinguishes it from sibling tools like generate_readme or create_google_doc, which handle only parts of the pipeline.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'full documentation pipeline' communicates a comprehensive workflow, and the sibling tool names suggest individual steps, so the intended use case is clear. However, it lacks explicit 'use this instead of X when...' guidance or exclusions, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosure. It states what the tool returns ('structured list of endpoints with methods, paths, handlers, and auth requirements'), which clarifies behavior. It does not mention side effects, but the return-focused wording suggests a read-only operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, each earning its place. The first sentence states the core purpose, and the second summarizes the output format. No unnecessary filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple single-parameter tool and the presence of an output schema, the description sufficiently covers the tool's purpose and expected output. It could be more complete with usage context or constraints, but it is adequate for selecting the 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 already fully describes the single parameter 'repo_path' with 100% coverage. The description does not add additional semantic meaning beyond what the schema provides, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Extract and document') and the resource ('API endpoints from a repository'). It also distinguishes from sibling tools like analyze_repository or generate_project_documentation by focusing on API endpoint discovery.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context that this tool is for API endpoint extraction from a repository, implying it is for that purpose. However, it does not explicitly mention when not to use it or name alternatives, so it lacks explicit usage guidance.
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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