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Tai-DT

Expo Gemini MCP Server

by Tai-DT
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TypeScript
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation4/5

    Tools are mostly distinct: search discovers relevant docs, get_expo_examples fetches code samples, and explain_expo_concept provides conceptual overviews. There is minor overlap between search and explain for concept queries, but the descriptions make the primary intent clear.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern: search_expo_docs, get_expo_examples, explain_expo_concept. This makes the intended action and target clear for each tool.

    Tool Count5/5

    With only 3 tools, the server is tightly scoped to its purpose of assisting with Expo documentation, examples, and concepts. Each tool serves a distinct needed function without redundancy.

    Completeness4/5

    The tool set covers the primary ways an agent would interact with Expo documentation: searching, retrieving examples, and getting explanations. A minor gap is the absence of a direct 'get_document' tool for known pages, but search effectively compensates.

  • Average 3.3/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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    Then . Browse examples.

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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

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'using Gemini AI' but does not explain what the response contains beyond 'detailed explanations,' nor does it address potential latency, need for external API calls, or any limitations. The behavior is under-specified.

    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 a single sentence, which is concise, but it relies on the vague phrase 'detailed explanations' and adds little substantive value. It is not overly long, yet it could be more informative without sacrificing brevity.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's low complexity and full schema coverage, the description is minimally adequate but not complete. It lacks guidance on when to use the tool, what output format to expect, and how the 'audience' parameter affects the explanation—gaps that matter more because no output schema or annotations exist.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The schema already provides descriptions for both parameters (concept and audience), with 100% coverage, so the baseline is 3. The description adds no additional parameter semantics beyond what the schema offers, and it does not elaborate on how 'concept' or 'audience' interact with the explanation's detail level.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's function: 'Get detailed explanations of Expo concepts using Gemini AI.' This uses a specific verb ('Get detailed explanations') and identifies the resource (Expo concepts), distinguishing it from sibling tools like search_expo_docs and get_expo_examples, though the added 'using Gemini AI' is implementation detail rather than core purpose.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    No guidance is provided on when to use this tool versus alternatives like search_expo_docs or get_expo_examples. The description implies use for explanations but lacks explicit when/when-not scenarios or mention of sibling tools, leaving the agent to infer the appropriate context.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

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

    With no annotations, the description carries the burden of behavioral disclosure. It mentions semantic search and Gemini AI enhancement, but these are also reflected in the useGemini parameter. It does not disclose return format, failure modes, or what 'enhancement' actually does, leaving significant behavioral ambiguity.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, front-loaded sentence with no filler or redundant information. It is appropriately concise for a search tool.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    There is no output schema, yet the description does not explain what the tool returns or how maxResults affects results. It also lacks any context about result format or behavior on empty queries, making it incomplete for an agent to predict the tool's output reliably.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The input schema has 100% coverage with clear descriptions for query, useGemini, and maxResults. The description adds little beyond the schema, only indirectly referencing Gemini enhancement, so the baseline score of 3 is appropriate.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool searches Expo documentation, with specific details about semantic search and Gemini AI enhancement. This distinguishes it from sibling tools like get_expo_examples and explain_expo_concept, which serve different purposes.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description gives no explicit guidance on when to use this tool versus the sibling tools. It only implies usage through the action 'Search through Expo documentation,' with no mention of alternatives, exclusions, or prerequisites.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior3/5

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

    With no annotations, the description must carry the burden of behavioral disclosure. It does mention 'using Gemini AI', which hints at generated content and potential non-determinism, but it does not describe the output format, any rate limits, or possible inaccuracies. This is adequate but not rich.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, concise sentence that front-loads the main action and object. Every word contributes to understanding the tool's purpose, with no wasted effort.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    The description is sufficient for a simple tool with only two parameters, but the absence of an output schema means it should clarify what a successful response looks like. It does not specify the structure or format of the returned code examples, leaving some ambiguity.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The schema already has 100% coverage, describing both 'component' and 'complexity' clearly. The description adds no additional parameter-level meaning, so it stays at the baseline of 3.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's function: to get code examples for specific Expo components or APIs. It includes a specific verb (get) and resource (code examples) and distinguishes itself from sibling tools like search_expo_docs and explain_expo_concept by focusing on example code.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description implies that this tool is for obtaining code examples, which is a clear context of use. However, it does not explicitly contrast with sibling tools or provide exclusions, so it misses the full 'when-not-to-use' guidance that would make it a 5.

    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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