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nakasyou

twitter_api_safe_relay_mcp

by nakasyou
OverviewSchemaRelated ServersScoreDiscussions
JavaScript

Server Quality Checklist

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

  • Latest release: v0.1.2

  • Disambiguation5/5

    The two tools have completely distinct purposes: one fetches/curates request templates from the catalog, the other executes a single request against the relay. There is no overlap or ambiguity between them.

    Naming Consistency3/5

    Both tools use a similar 'twitter_' prefix, but one uses 'request' and the other 'request_catalog', which is a mild inconsistency in granularity. The names are readable but the pattern isn't perfectly parallel—'twitter_api_request' vs 'twitter_request_catalog' mixes the placement of 'api' and 'request'.

    Tool Count2/5

    Two tools feels extremely thin for a Twitter/X API surface, which is vast (timelines, tweets, users, friendships, DMs, media, trends). While the catalog tool cleverly bundles the numerous request types into a searchable interface, the overall surface is still very limited given the breadth of the domain.

    Completeness3/5

    The two-tool design is a clever pattern: the catalog surfaces templates for any operation and the executor handles them, so in principle the coverage is as deep as the catalog. However, there are no helper tools for authentication setup, session management, or error diagnosis, and the design forces agents to do two calls for every single operation, creating friction.

  • Average 4.5/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 7 commits in the last 12 weeks
    • Last stable release on
    • 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.

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    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

  • Behavior4/5

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

    No annotations exist, so the description carries the full burden. It discloses that reads and writes share the relay, warns about side effects ('confirm before side effects'), advises verifying writes, and explicitly warns that HTTP 200 may still contain GraphQL errors. This is meaningful behavioral context beyond what schema could convey.

    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 tight sentences, each earning its place: purpose, workflow guidance referencing the catalog, and a critical behavioral warning about GraphQL errors. Zero filler, front-loaded with the actionable catalog-first directive.

    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?

    It's a general-purpose request relay tool with 5 params and no output schema. The description wisely points to twitter_request_catalog for operation-specific details, which offloads completeness. The side-effect verification guidance is appropriate for a mutation-capable tool. Lacks some detail about response handling given no output schema, but the catalog referral covers that gap.

    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 80%, which is high, so the baseline is 3. The description adds some context about endpoint structure (e.g., '/i/api/graphql/...') but doesn't go beyond the schema's own descriptions. With high schema coverage, the description needn't compensate heavily, though it could note that method defaults to GET and confirms the params/headers semantics.

    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 executes a single request against a Twitter/X relay, with a specific verb ('execute one request') and resource ('signed-in Twitter/X relay'). It differentiates itself from the sibling twitter_request_catalog by instructing to get templates from that catalog first, making the distinction between the two tools 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 provides explicit usage context: get a current template from twitter_request_catalog first, confirm before side effects, and verify writes with follow-up reads. It doesn't explicitly state when NOT to use this tool, but the alternative is clearly referenced and the workflow (catalog-first) is well articulated.

    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 carries the full burden and discloses significant behavioral details: it fetches the upstream NDJSON on every call (network dependency, ensures freshness), keeps query IDs and feature flags current, and returns ranked templates. This transparency about the live-fetch behavior and output format goes well beyond what's needed.

    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 cover purpose, usage examples, output format, and behavioral transparency without waste. It's front-loaded with the core purpose in the first sentence and efficiently packs useful operational details into the remaining two. Every sentence earns its place.

    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 compensates by stating results are ranked templates in the argument shape for twitter_api_request, which sufficiently communicates the return type for a search tool. With a simple 2-param schema at 100% coverage and rich behavioral disclosure, the description is complete for this tool's 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% and both parameters have descriptions in the schema. The description adds context that query terms must match all space-separated terms (AND semantics beyond case-insensitivity) and that results are ranked templates. This adds marginal value beyond the schema but is light on parametrization details like how limit interacts with ranking.

    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 a catalog by operation name, path, method, or content, with specific operation examples (SearchTimeline, UserByScreenName, etc.). It also clarifies the key output property: templates already converted to the argument shape for twitter_api_request, which distinguishes its purpose from the sibling tool that actually executes requests.

    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 provides clear usage context: use operation-like terms to search the catalog. It names the sibling tool (twitter_api_request) and frames results as pre-converted arguments for it, establishing when to use this tool (for discovering templates) vs. the sibling (for making requests). However, it doesn't explicitly state when NOT to use it or list alternative search approaches.

    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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