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upstash

Context7 MCP

by upstash
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Server Quality Checklist

75%
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  • Latest release: v1.0.17

  • Disambiguation5/5

    Each tool has a distinct and complementary purpose: one resolves library names to IDs, the other queries documentation using that ID. There is no overlap.

    Naming Consistency5/5

    Both tools follow the same verb_noun pattern with snake_case: 'resolve-library-id' and 'query-docs'. Consistent and predictable.

    Tool Count4/5

    With only two tools, the surface is minimal but still covers the core workflow for querying documentation. It is slightly thin but appropriate for a focused server.

    Completeness4/5

    The two tools form a complete workflow: resolve then query. No obvious gaps for the stated purpose, though additional tools like list_libraries could enhance completeness.

  • Average 4.6/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • 617 of 638 community issues answered or closed in the last 6 months
    • 105 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is failing

  • 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.

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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

  • Behavior5/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    Adds behavioral context beyond the readOnlyHint annotation: the 3-call limit, prerequisite step, and warning against sensitive data. No contradiction 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?

    Three short paragraphs each serving a distinct purpose: purpose, prerequisite, limitation. Front-loaded with the core action, no redundant information.

    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 prerequisite, usage limit, and parameter guidance. Lacks explicit description of output format, but since the tool retrieves documentation and code examples, the output type is reasonably inferable.

    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 provides 100% coverage with detailed descriptions for both parameters. The tool description reinforces the relationship between libraryId and resolve-library-id but adds little semantic meaning beyond what's already in 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 retrieves and queries documentation and code examples from Context7 for any library, distinguishing it from the sibling 'resolve-library-id' tool which is for obtaining library IDs.

    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 use 'resolve-library-id' first unless user provides library ID, and imposes a 3-call limit per question, providing clear guidance on when and how many times to use.

    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 readOnlyHint=true, which is consistent with the tool's purpose. The description adds important behavioral details beyond annotations, such as a 3-call limit per question, handling of ambiguous queries, and a warning not to include sensitive information in the 'query' parameter.

    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, but it is somewhat lengthy. It front-loads the essential purpose and usage note, but the selection process details could be more succinct. Still, it remains clear and organized.

    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 there is no output schema, the description adequately explains the response format. It covers edge cases like multiple matches, no matches, and ambiguous queries, providing complete guidance for the agent to handle various 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?

    Schema coverage is 100%, so the baseline is 3. The description adds value by explaining the role of each parameter: 'libraryName' is the name to search for, and 'query' is the user's original question used for ranking. It also includes a critical warning about sensitive data in 'query', which enhances understanding.

    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 that the tool resolves a package/product name to a Context7-compatible library ID. It distinguishes itself from the sibling tool 'query-docs' by noting it must be called first, and includes specific details about selection criteria and response format.

    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 call this tool: before 'query-docs' unless the user provides a library ID in a specific format. It also provides a detailed selection process and response format, guiding the agent on how to use the tool correctly.

    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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