Context7 MCP
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The two tools have clearly distinct purposes: 'resolve-library-id' resolves package names to library IDs, while 'get-library-docs' fetches documentation using those IDs. There is no overlap in functionality, and the descriptions explicitly define their roles and interdependencies, making it impossible to confuse them.
Naming Consistency5/5Both tools follow a consistent verb-noun pattern with kebab-case (e.g., 'get-library-docs', 'resolve-library-id'), using clear action verbs ('get', 'resolve') paired with specific nouns. This consistency makes the tool set predictable and easy to understand at a glance.
Tool Count3/5With only two tools, the server feels thin for its apparent domain of library documentation retrieval. While the tools cover core functions (resolving IDs and fetching docs), typical documentation servers might include additional operations like searching, listing libraries, or managing versions, suggesting a borderline under-scoped surface.
Completeness4/5The tool set covers the essential workflow for fetching library documentation: resolving IDs and retrieving docs. However, there are minor gaps, such as no tools for searching libraries directly, listing available libraries, or handling documentation updates, which agents might need to work around but don't break core functionality.
Average 4.1/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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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
- 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 mentions the need for a 'Context7-compatible library ID' and implies it fetches documentation, but lacks details on behavioral traits like rate limits, error handling, or what 'up-to-date' means. It adds some context (e.g., ID format requirements) but is incomplete for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded: it starts with the core purpose, then immediately provides critical usage guidelines. Both sentences are essential—the first defines the tool, and the second explains prerequisites—with no wasted words, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is moderately complete. It covers the purpose and usage well but lacks details on behavior (e.g., response format, errors) and output. For a tool with 3 parameters and no structured safety hints, it should do more to compensate, leaving gaps in contextual understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by mentioning the ID format and prerequisite, but doesn't provide additional semantics for parameters like 'topic' or 'tokens'. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Fetches up-to-date documentation for a library.' It specifies the resource (library documentation) and the action (fetching). However, it doesn't explicitly differentiate from the sibling 'resolve-library-id' beyond mentioning it as a prerequisite, so it falls short of a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does 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 vs. alternatives: it states that 'resolve-library-id' must be called first unless the user provides a library ID directly. This clearly defines the prerequisite and alternative scenarios, making it easy for an agent to decide when to invoke this tool.
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 provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it returns a list of matching libraries, explains the selection process (prioritizing exact matches, description relevance, documentation coverage, trust score), and outlines the response format (including handling of multiple matches, no matches, and ambiguous queries). However, it doesn't mention potential limitations like rate limits or authentication needs, which keeps it from a perfect score.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose and usage guidelines, but it includes extensive procedural details (e.g., 'Selection Process' and 'Response Format' sections) that, while informative, make it verbose. Some sentences, like those detailing the selection criteria, could be more concise. It earns its place but could be streamlined for better efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (involving a selection process and response formatting) and the absence of annotations and output schema, the description does a good job of covering key aspects: purpose, usage, behavior, and response handling. However, it lacks details on error cases beyond 'no good matches' and doesn't specify the exact structure of the returned list, leaving some gaps in completeness for a tool with no output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the parameter 'libraryName' well-documented as 'Library name to search for and retrieve a Context7-compatible library ID.' The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. Given the high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't need to.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Resolves a package/product name to a Context7-compatible library ID and returns a list of matching libraries.' It uses specific verbs ('resolves', 'returns') and distinguishes from its sibling 'get-library-docs' by explaining this is a prerequisite step. The purpose is unambiguous and well-articulated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidelines: 'You MUST call this function before 'get-library-docs' to obtain a valid Context7-compatible library ID UNLESS the user explicitly provides a library ID in the format '/org/project' or '/org/project/version' in their query.' It clearly states when to use this tool versus alternatives (direct ID usage) and references the sibling tool, making the guidance comprehensive and actionable.
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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