ws-mcp

by rm-rf-prod

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v7.4.1
Disambiguation5/5
Each tool has a clearly distinct purpose: audit scans code, auto_scan detects dependencies, batch_resolve resolves multiple libraries, best_practices fetches guidance, changelog gets release notes, compare compares libraries, compat checks runtime support, dispatch routes ambiguous queries, examples finds real-world code, get_docs fetches documentation, migration provides upgrade guides, resolve_library gets library IDs, search handles arbitrary topics, and snippets returns code snippets. No significant overlap.
Naming Consistency5/5
All tools follow the consistent pattern 'gt_verb_noun' or 'gt_verb' in snake_case, e.g., gt_audit, gt_batch_resolve, gt_dispatch. No mixing of conventions or irregular naming.
Tool Count5/5
With 14 tools, the set is well-scoped for a code quality and documentation server covering auditing, dependency scanning, library resolution, documentation fetching, comparisons, compatibility, examples, migrations, and search. Each tool earns its place, and the number is neither too small nor overwhelming.
Completeness5/5
The set covers the full lifecycle of developer informational needs: finding issues, getting best practices, comparing libraries, checking compatibility, retrieving documentation, code examples, migration guides, changelogs, and resolving library names. There are no obvious gaps for its stated purpose of assisting with code analysis and library documentation.
Average 4.5/5 across 14 of 14 tools scored. Lowest: 3.7/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 18 commits in the last 12 weeks
- Last stable release on August 5, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Elastic License 2.0.
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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds that the tool outputs a routing decision (tool name, args, reason, confidence) and embeds a routing table, not executing the target tool. This provides useful behavioral context beyond 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?
The description is well-structured with clear sections (WHEN TO USE, WHEN NOT TO USE, OUTPUT) and front-loaded with the main action. It is slightly verbose but every sentence adds value.
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 (2 params, no output schema), the description adequately covers purpose, usage, output format, and examples. It could be more precise about the routing logic, but it is sufficient for an agent to understand and use 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% with both parameters well-documented. The description reinforces with examples but does not add new semantics beyond the schema, so 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 routes plain-text user queries to the correct gt_* tool with right arguments, giving multiple examples. It explicitly distinguishes itself as a single entry point from sibling tools that are direct calls.
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 includes explicit 'WHEN TO USE' (ambiguous intent, 'use gt' without tool) and 'WHEN NOT TO USE' (already know which tool), advising direct calls to save a round-trip. This provides clear decision criteria for the agent.
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?
Annotations already convey safe, idempotent, read-only behavior. The description adds valuable context: the 3-call-per-question limit and a proprietary data notice. This exceeds annotations without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is comprehensive but lengthy, with sections that could be condensed. While well-structured, some parts (like the selection process and response format) are redundant with logical inference. It earns its place but is not optimally concise.
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's moderate complexity and absence of an output schema, the description is fully complete. It covers all necessary aspects: prerequisites, alternatives, selection logic, response format, and usage limits.
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% and both parameters are well-described. The description reiterates the library name parameter and adds context about the query parameter's purpose, but does not significantly expand beyond schema details. 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's purpose: 'Resolve a package/product name to a Context7-compatible library ID and returns matching libraries.' It also explains its role relative to sibling tools like gt_get_docs and gt_batch_resolve, making it highly distinct.
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 usage guidance is provided: 'You MUST call this function before gt_get_docs...' and 'For 2-20 libraries at once, use gt_batch_resolve instead.' The selection process and response format are detailed, leaving no ambiguity.
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?
Annotations already declare readOnlyHint, idempotentHint, and not destructive. The description adds context: reads specific config files, fetches best practices for dependencies, lists unrecognized dependencies separately. No contradiction. Could mention output format or rate limits but sufficient.
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?
Four sentences: purpose, file list, usage alternative, edge case. Each sentence adds value. Front-loaded with the core action. No fluff.
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 what files are read, what happens with dependencies, how to invoke, alternative tool, and edge case. No output schema, but description gives enough for an agent to understand the tool's behavior. Could mention return value structure.
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 3 parameters have descriptions in schema (100% coverage). Description adds little beyond the schema descriptions, only implicitly connecting projectPath to file reading. 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 'Automatically detect all dependencies in a project and fetch latest best practices for each', and specifically distinguishes from sibling 'gt_audit' by directing source code scanning to that tool.
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 says to use this tool for dependency scanning and best practices, and provides an alternative: 'to scan your own source code for issues, use gt_audit instead'. Also notes that unrecognized dependencies are listed separately and never fail.
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?
Annotations already provide readOnlyHint, destructiveHint, etc. Description adds behavioral detail on data source priority (GitHub Releases API first, then CHANGELOG.md, then docs site). 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?
Very concise: two short paragraphs, front-loaded with purpose, no redundant words.
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, sources, usage context, sibling differentiation. No output schema, but description doesn't detail return format; however, given the tool's simplicity, it's adequate.
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 description adds minimal extra meaning. It notes libraryId is from gt_resolve_library and version is a prefix filter, but schema already says that.
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 'Fetch recent release notes and changelog for a library' with specific verb and resource. Distinguishes from sibling tool gt_migration by mentioning alternative use case.
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 says 'Use before upgrading' and gives when-to-use ('what changed in version X') and when-not-to-use ('how do I upgrade... use gt_migration instead').
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?
Annotations indicate read-only, idempotent, and non-destructive behavior. Description adds that it 'Fetches live data', implying network usage and potentially changing results. No contradictions, but could mention rate limits or response format.
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?
Two concise paragraphs: first explains purpose and data sources, second provides usage guidance and alternatives. No redundant information, well-structured and front-loaded.
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, good annotations, and comprehensive schema, the description covers all necessary aspects: purpose, usage, and data sources. Could mention output format but not essential.
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%, with detailed descriptions for each parameter. The description does not add additional meaning beyond the schema, so 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 title and description clearly state that the tool checks browser/runtime compatibility for web APIs, CSS, and JavaScript syntax. It specifies data sources (MDN, caniuse) and distinguishes it from sibling tools like gt_get_docs and gt_best_practices.
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 states when to use: 'when the question is specifically about which browsers or runtimes support a feature' and what not to use for: 'not a library name'. Provides clear alternatives (gt_get_docs, gt_best_practices).
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?
Annotations already provide read-only, open-world, idempotent hints. Description adds context: year normalization, auto-detection from project context, and token constraints. 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with bullet points and headings. Front-loaded with main purpose. Slightly long but every sentence adds value. Could be tightened slightly, but highly functional.
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?
Rich description covers purpose, usage guidelines, parameters with examples, and behavioral context. No output schema but annotations and schema cover remaining needs. Complete for a search tool with many use cases.
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 detailed descriptions for both 'query' and 'tokens'. Description reinforces 'query' with many examples but does not add new meaning beyond schema. Baseline 3 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?
Description uses 'Search for latest best practices, docs, or guidance on ANY topic' with specific verb 'Search' and resource 'any topic'. It distinguishes from siblings by naming alternative tools for specific library queries.
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 states when to use gt_search vs alternatives: 'If the query names ONE specific library, prefer gt_resolve_library + gt_get_docs/gt_best_practices...' and also mentions gt_compat and gt_examples. Clear context with examples.
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?
Annotations already provide safety and idempotency hints. Description adds that it does registry-only lookup (no external fallback) and max 20 per call. This adds useful behavioral context beyond 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?
Three concise sentences, front-loaded with action. No wasted words. Each sentence provides essential information.
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 simple parameter and rich annotations, the description covers purpose, usage, behavioral constraints, and context for a complete 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 coverage is 100% with description. Main description does not add significant meaning beyond the schema's example and constraints. Baseline 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?
Clearly states it resolves multiple library names to IDs and docs URLs in a single call. Distinguishes from siblings like gt_resolve_library (single with fallback) and gt_auto_scan (project scanning).
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 specifies when to use (batch resolution before gt_get_docs) and when not to (single library: gt_resolve_library; project scanning: gt_auto_scan). Provides clear 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?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. Description adds valuable context: audit patterns run locally, only fix guidance fetch requires network, and potential token issues. 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is well-structured with front-loaded purpose, sibling distinction, category list, usage examples, and troubleshooting. While slightly lengthy, each sentence adds value and the organization aids readability.
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 no output schema, description specifies return type (file:line locations). Covers purpose, parameter semantics, usage guidance, and edge cases (token limits). Could mention more about output structure, but overall complete for an audit 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?
Schema covers 100% of parameters with descriptions. Description enriches parameters by listing all 18 categories plus 'all', showing usage patterns for mobile and web apps, explaining default behaviors (tokens, maxFiles, projectPath), and clarifying the token parameter's purpose.
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?
Description clearly states it scans source files for issues across 18 categories and fetches live best-practice fixes, returning file:line locations. It explicitly distinguishes from sibling gt_auto_scan by specifying 'this audits YOUR OWN source code' vs dependencies.
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?
Provides explicit when-to-use guidance: contrasts with gt_auto_scan, gives recommended category combinations for mobile apps and web apps, and advises on GT_GITHUB_TOKEN troubleshooting when doc fetches fail.
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?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: a proprietary data notice (licensed registry, non-extraction requirement) and a usage limit. No contradictions with 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 concise and well-structured: purpose first, then usage guidelines, then an important notice, then a usage limit. Each sentence adds value, and the total length (~150 words) is appropriate.
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 has 4 parameters, no output schema, and no nested objects, the description covers usage guidelines, behavioral restrictions, and parameter context sufficiently. The proprietary notice adds necessary completeness for legal/compliance awareness.
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 description coverage is 100% with descriptions for all 4 parameters. The description adds meaningful context beyond the schema, such as the possible formats for libraryId (from gt_resolve_library, npm:pypi:, or library name) and that topic can be left empty.
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 fetches best practices, patterns, and guidelines for a library or framework, targeting specific content types (best-practices pages, guides, migration docs, performance tips) while excluding generic reference docs. It distinguishes from the sibling tool gt_search by specifying when to use each.
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 preferring this tool over gt_search when the question centers on one resolvable library, and using gt_search for cross-cutting or non-library topics. Also includes a hard usage limit of 3 calls per question, providing clear when-not-to-use guidance.
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?
Annotations already declare readOnlyHint=true, destructiveHint=false, etc. The description adds that it fetches 'live documentation' and resolves internally, which provides context beyond annotations. Lacks details on how content is selected but sufficient.
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?
Two concise paragraphs: first states core function, second gives usage guidance and alternative. Front-loaded with key action and resource.
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?
No output schema, but description indicates it presents relevant content. Input constraints (2–3 libraries) are clear. Could mention output format but not required. Complete enough for a comparison 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 have schema descriptions (100% coverage), but the description adds valuable context: clarifies that 'libraries' must be names not IDs, provides exemplars, and explains token defaults and range.
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 compares 2–3 libraries side-by-side by fetching live documentation. It distinguishes itself from the sibling tool 'gt_get_docs' by specifying that this tool is for comparisons, not single library docs.
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 states when to use (e.g., 'X vs Y' questions) and when not to (use gt_get_docs instead). Provides clear guidance on input format (library names not registry IDs).
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?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true. The description adds valuable context beyond these: it requires a GT_GITHUB_TOKEN env var for higher rate limits (5000 req/hr vs 60 unauthenticated) and clarifies the data source is open-source GitHub repositories. It does not detail error handling when the token is missing, but overall it adds significant 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 very concise: two sentences in the first paragraph and two in the second. Every sentence adds value: purpose, differentiation from sibling, environment variable requirement, and source. No redundant or extraneous information. It is well-structured and front-loaded with the core purpose.
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's complexity (search over GitHub with rate limiting and library differentiation), the description covers all necessary context: what it does, when to use it, alternative tool, authentication/env var requirement, source of data, and a note on rate limits. Even without an output schema, the description provides enough for an agent to understand the tool's behavior and constraints.
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 has 100% description coverage for all 4 parameters, so the baseline is 3. However, the description adds concrete examples for 'library' (e.g., 'drizzle-orm', 'fastapi') and 'pattern' (e.g., 'middleware', 'useMutation'), which help clarify the intended use of these parameters beyond the schema descriptions. This extra context justifies a 4.
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 searches GitHub for real-world usage examples of libraries or patterns, with specific verb 'Search' and resource 'GitHub'. It explicitly distinguishes itself from the sibling tool gt_snippets, which covers library's own documentation. The title 'Find Real-World Code Examples' also aligns well.
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 provides explicit guidance on when to use this tool: 'Use this when you want to see how real projects use a library.' It also tells when not to use it by pointing to an alternative: 'For code snippets extracted from the library's own documentation, use gt_snippets instead.' This makes the usage context very clear.
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?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the description doesn't need to cover safety. It adds behavioral context by specifying the types of documents it targets (MIGRATION.md, CHANGELOG, etc.), which is useful beyond 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?
Three sentences, each serving a distinct purpose: what it does, prerequisite, and when to use vs alternative. No redundancy, perfectly front-loaded.
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 4-parameter input schema with 100% coverage and comprehensive annotations, the description covers all necessary context: what it retrieves, prerequisite, and differentiation from sibling. No output schema needed as the description clarifies the return type.
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 description coverage is 100%, so schema already documents all parameters. The description adds value by explaining that libraryId comes from gt_resolve_library and reinforces the version parameters. This provides contextual meaning 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?
The description clearly states it fetches migration guides, breaking changes, and upgrade instructions for a library, specifying target files like MIGRATION.md. It distinguishes from sibling tool gt_changelog, which is for release notes without upgrade instructions.
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 states when to use: when user asks HOW to upgrade step-by-step. Also provides a clear exclusion: for release notes without upgrade instructions, use gt_changelog instead. Includes prerequisite to call gt_resolve_library first.
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?
Adds significant context beyond annotations: first-call indexing, disk cache, refresh semantics, version precedence, source priority (llms.txt > Jina > GitHub README), and a proprietary data notice. Aligns with 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?
Well-structured into logical paragraphs (purpose, usage, caching, sibling distinction, legal notice). Slightly verbose but every sentence adds value. Could tighten, but effective.
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 caching, versioning, source priority, sibling distinction, and output format (Context7-compat). No output schema, but description lists fields. Lacks mention of maxSnippets in description, but schema covers it. Adequate for complexity.
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 100%, so baseline is 3. The description adds operational meaning: examples for topic, cache behavior tied to version, projectPath auto-detection interaction, and maxSnippets default/max. Exceeds baseline.
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 returns ranked code snippets for a library + optional topic, distinguishing itself from sibling gt_examples (real open-source projects vs own docs). Specific verb and resource.
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 use for focused code examples instead of full doc pages, and directs to gt_examples for real project code. Also explains caching, refresh, and versioning behaviors.
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?
Discloses prioritization order (llms.txt, Jina Reader, GitHub README) and includes a proprietary data notice. Annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false) are consistent and no contradiction.
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 sections (main purpose, alternatives, notice, limit). Slightly verbose but front-loaded and efficient overall.
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 all necessary aspects: prerequisite (resolve library), parameter details, fallback order, usage limits, proprietary notice. No gaps given high schema coverage and annotations.
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 100%, so parameters are well-documented. The description adds value by explaining how libraryId is obtained and how projectPath auto-detects version. Additional context beyond 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 'Fetch up-to-date documentation for any library or framework' with specific verb and resource. It distinguishes from siblings (gt_best_practices, gt_snippets) by explaining when to use those alternatives.
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 instructs to call gt_resolve_library first, provides context for alternatives, and includes a usage limit (no more than 3 times per question).
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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