ie-mcp

by JonasKlimt

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 have distinct purposes, but fetch_latest_docs and get_doc_section overlap in retrieving documentation sections, with nuanced differences in caching behavior. The descriptions help disambiguate but some confusion may remain.
Naming Consistency5/5
All tool names follow a consistent verb_noun snake_case pattern (e.g., fetch_latest_docs, get_package_info, search_source), making them predictable and easy to understand.
Tool Count5/5
With 11 tools, the set is well-scoped for the server's purpose of managing Python documentation and source code. Each tool serves a clear function without unnecessary bloat.
Completeness4/5
The tool set covers the main workflows: listing packages, retrieving documentation and source code, searching, and version management. Minor gaps like a missing 'get_docs_homepage' tool are mitigated by fetch_latest_docs's behavior.
Average 4.5/5 across 11 of 11 tools scored. Lowest: 3.8/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 7 commits in the last 12 weeks
- Last stable release on June 30, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under BSD 3-Clause.
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 full burden. It only states the basic purpose of listing packages, but does not disclose any behavioral traits such as whether the operation is read-only, pagination behavior, rate limits, or authentication requirements.
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 of 13 words with no unnecessary information, perfectly sized for its simple purpose.
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 parameterless list operation with an output schema (not shown), the description is sufficiently complete. It conveys the essential purpose without needing to detail return values. However, it could mention that the list is of packages with documentation, which it does.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has no parameters (input schema is empty), so the description need not add parameter meaning. With 0 parameters, baseline is 4; the description is adequate.
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 verb 'list', the resource 'Python packages', and the scope 'that this MCP server has documentation for'. It is specific and distinguishes from sibling tools like get_package_info which would return details for a single package.
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 the use case of enumerating documented packages, but provides no explicit guidance on when to use this tool versus alternatives like get_package_info or search_docs, nor any conditions when not to use it.
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?
Since no annotations are provided, the description carries the full burden. It clearly indicates that the tool retrieves metadata without side effects, and lists the exact pieces of metadata returned. This is transparent for a read-only operation, though it does not mention error conditions or pagination.
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: two sentences covering purpose and parameter, with no fluff. It is front-loaded with the core action and easily scannable.
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?
Despite lacking an output schema, the description enumerates the returned metadata fields, which is sufficient for an agent to understand what to expect. It does not specify the exact structure (e.g., JSON keys), but given the tool's simplicity, this is adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema only defines 'package' as a string with no description, yielding 0% schema coverage. The description compensates by adding context: 'Package name as returned by list_packages()'. This provides valuable usage guidance beyond the raw schema.
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 'Get metadata for a package' and enumerates specific items (description, GitHub URL, docs URL, install commands, available documentation sections). It effectively distinguishes from siblings like list_packages (which returns package names) and fetch_latest_docs (which fetches 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 provides a usage hint by noting that the 'package' argument should come from 'list_packages()'. However, it does not explicitly state when to use this tool versus alternative siblings like get_doc_section or get_function_source.
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?
The description mentions it 'Queries the ReadTheDocs API to discover published versions,' indicating a network call. No annotations are provided, so the burden is on the description. It does not disclose potential side effects, authentication needs, or rate limits, but as a read-only list operation, this is moderately acceptable.
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: two sentences plus an Args block. It front-loads the purpose, then adds usage context and parameter guidance. Every sentence serves a distinct purpose without 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, output schema exists), the description adequately covers the core function and usage context. It lacks details on error cases or network dependencies, but it is sufficient for an agent to select and invoke the tool correctly in most scenarios.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema contains only the 'package' parameter with no description. The description adds 'Package name as returned by list_packages().' This provides a helpful source hint, guiding the agent to use the correct value format. Schema coverage is 0%, so this compensation is valuable.
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 begins with 'List all available documentation versions for a package,' which is a specific verb+resource combination. It further distinguishes the tool by stating it should be called before get_doc_section or search_docs for non-latest versions, showing its unique prerequisite role among siblings.
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 explicitly states 'Always call this before get_doc_section or search_docs when the user asks about a specific version other than the latest.' This gives clear when-to-use guidance. It does not mention when not to use it, such as for the latest version, but the context implies alternatives.
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?
Discloses behavior: uses GitHub API, returns up to 10 results with file name, path, URL. Notes authentication requirements and rate limits. Lacks detail on error handling or edge cases, but is adequate given no annotations.
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?
Reasonably concise with a clear structure: main description followed by argument explanations. No extraneous information, though could be slightly more streamlined.
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?
Covers purpose, parameters, output format, and authentication. Given the tool has only 2 parameters and no annotations, the description is largely complete. Missing error conditions or version scope, but acceptable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has no descriptions (0% coverage). The description adds meaning: 'package' is a package name from list_packages(), 'query' is a search string example. Provides helpful context beyond the schema.
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?
Clearly states it searches GitHub source code of a package for a query string using the GitHub Code Search API. It specifies the verb 'search' and resource 'source code of a package', and distinguishes from sibling tools like search_docs and get_function_source.
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?
Provides clear context: requires GITHUB_TOKEN, rate limits (5000 vs 60 req/hr). However, does not explicitly state when to use this versus alternative sibling tools like search_docs or get_function_source, though the purpose differentiation is implied.
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?
No annotations provided, description carries full burden. It discloses cache bypassing, caching of fetched page, and behavior for section vs homepage. Lacks mention of potential errors or network implications, but adequate for a fetch tool.
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?
Concise paragraph with clear purpose first, then details and Args section. Each sentence adds value. Could be slightly tighter but well-structured.
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?
Covers main behaviors: cache bypassing, caching, fetching homepage or section, parameter defaults. Output schema exists so return values need no explanation. Complete for a 3-param tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All three parameters are well explained with examples ('introduction', 'latest') and cross-references (list_packages(), list_doc_versions()). Adds value beyond schema's basic types.
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 it force-fetches documentation from the web bypassing cache, and distinguishes from siblings by emphasizing freshness. It also specifies fetching a section or the homepage.
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?
Explicitly says use when user wants up-to-date content or stale cache. Does not list alternatives among siblings but provides context by mentioning caching behavior.
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?
Describes return values (version and Python path) and behavior when package is not installed (guidance on mcp.json). No annotations exist, so description carries full burden. Could mention that it is read-only and has no side effects.
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?
Reasonably concise; covers purpose, usage, return, and error handling in a few sentences. Could be slightly tighter by removing redundant phrasing like 'so users can verify which environment is being checked'.
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 one parameter, no output schema, and no annotations, the description is highly complete: explains what it does, what it returns, when to use it, and what happens in error cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage, but description adds meaning: 'Package name as returned by list_packages()'. This links the parameter to the sibling tool list_packages, providing helpful context beyond the schema.
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?
Clearly states the verb 'Check what version' and the resource 'package installed in the Python environment'. Distinguishes from siblings by specifying when to call this tool before get_function_source or get_doc_section.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly tells when to use this tool: 'Always call this before get_function_source or get_doc_section when the user has not specified a version'. Provides context about environment verification and error handling.
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 full burden. It discloses that missing pages are fetched live and cached before searching, returns up to 10 case-insensitive paragraph-level snippets with section names and text ≤500 chars, and defaults to latest version. It does not mention rate limits or auth, but the caching and search behavior are well described. No contradictions.
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 concise, with each sentence adding value. It front-loads the core purpose, then adds caching and output details, finally listing parameters in an Args block. No wasted words.
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 3 parameters, no annotations, and an output schema, the description covers all necessary aspects: purpose, caching behavior, output format (up to 10 snippets with section name and snippet ≤500 chars), and parameter semantics. The agent can correctly invoke this tool without further clarification.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so description adds essential meaning: package is 'Package name as returned by list_packages()', query is 'Free-text search query', version is 'Docs version slug (default: "latest")' with guidance to use list_doc_versions() to discover versions. This fully compensates for the lack of schema descriptions.
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 searches package documentation for a query string, with specific details about caching and return format. It distinguishes itself from sibling tools like get_doc_section or fetch_latest_docs by focusing on search across documentation, not fetching a single section or full docs. The verb 'search' and resource 'package documentation' are explicit.
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 advises to call list_doc_versions(package) first if the user asks about a specific older version, providing clear context for when to use this tool versus list_doc_versions. However, it does not explicitly state when to avoid this tool or compare it to other search or fetch tools among siblings.
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?
No annotations provided, so description carries full burden. It discloses caching behavior, version tag fetching, and error conditions. However, it does not mention potential rate limits or authentication needed for GitHub access.
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?
Well-structured with clear paragraphs and bullet-like Args list. Front-loaded with purpose. Could be slightly more concise, but every sentence adds value and there is no redundancy.
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?
Despite no output schema, the description outlines the return value ('full source, including docstring and signature') and error conditions. Given the complexity (caching, versioning), it is complete and thorough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates. Each parameter is explained: package as returned by list_packages(), function_name as exact name, version with examples and default. This adds meaning beyond the schema titles.
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 action: 'Find a function ... and return its full source'. It specifies the resource (function source code) and distinguishes from siblings by referencing list_source_versions for version verification.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly guides when to use the tool and what prerequisites exist: 'call list_source_versions(package) first to confirm the version tag exists'. It also explains when errors are returned, providing clear context.
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?
No annotations provided, so description carries full burden. It discloses the operation (listing) and is straightforward. However, it does not mention potential pagination or limitations, but for a simple list tool this is adequate.
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?
Very concise: 3 sentences total. Front-loaded with purpose, then usage note, then parameter description. No unnecessary words.
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 an output schema exists, description need not explain return values. It fully explains the single input parameter with a reference to another tool, making it complete for a simple list operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but description adds crucial context: 'Package name as returned by list_packages().' This tells the agent how to obtain the parameter value, compensating for the lack of schema description.
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 'List all available source code versions (GitHub tags) for a package.' It uses a specific verb ('list') and resource ('source code versions'), and distinguishes from siblings like 'get_function_source' and 'list_packages'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance: 'Use this when the user asks about a specific version of a function, to confirm the version exists before calling get_function_source.' It tells when to use and names an alternative tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses the tool's behavior: it returns the Python executable path, version, and installed versions of every package. No side effects are expected; it is a read-only inspection tool, and the description accurately reflects that.
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 concise, consisting of two short sentences that front-load the purpose. Every sentence serves a clear function: stating what it returns and when to use it. No unnecessary words.
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 tool has no parameters and no output schema, the description is complete. It explains exactly what the tool returns and provides usage context. No additional information is needed for an agent to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so there are no parameters to describe. The baseline for no parameters is 4. The description does add value by explaining the return values, which is relevant but falls under parameter semantics only indirectly.
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 it inspects the MCP server's Python environment, listing the Python executable path, version, and installed packages. This distinguishes it from sibling tools like get_installed_package_version and list_packages, which have narrower scopes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells when to use the tool (to verify the correct environment before asking about specific packages) and what to do if a package is not installed (update command in .vscode/mcp.json). This provides clear context and actionable guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but the description fully discloses caching behavior: 'The first call fetches the live docs page and caches it locally. Later calls are served from the cache.' It also implies a read-only, non-destructive 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 starts with a concise purpose, then uses clear paragraphs for usage guidelines and parameter details. Every sentence adds value without redundancy.
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 presence of an output schema, the description does not need to detail return values. It covers caching, prerequisites, and parameter sources, making it fully complete for an agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description compensates by explaining each parameter: package as from list_packages(), section with examples, and version with default and reference to list_doc_versions.
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 states 'Return the full Markdown content of a specific documentation section' with a clear verb and resource. It distinguishes from sibling tools like search_docs and fetch_latest_docs by specifying it returns a single section's content and mentions caching behavior.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises calling get_package_info first to discover sections and list_doc_versions before using a specific version. This guides the agent on prerequisites and when to use alternative tools.
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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