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Rodriguespn

Logflare MCP Code Mode

by Rodriguespn
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TypeScript
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Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The three tools have clearly distinct purposes: execute_read for read-only GET requests, execute_write for state-changing requests, and search for exploring the API spec. There is no overlap.

    Naming Consistency3/5

    The naming pattern is inconsistent: two tools use the 'execute_' prefix (execute_read, execute_write), while the third is simply 'search', breaking the verb_noun pattern.

    Tool Count3/5

    Three tools is borderline for a server that aims to cover a full API. While the number is not excessive, it feels minimal, and a typical server might include more dedicated tools instead of relying on generic code execution.

    Completeness5/5

    The tool set covers all possible operations on the Logflare Management API: read, write, and spec exploration. Users can perform any CRUD action via execute_read and execute_write, making the surface complete.

  • Average 4.9/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

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  • This repository includes a README.md file.

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

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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?

    Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds that only GET requests are allowed, that certain endpoints (access-tokens, backends, teams) are blocked, and describes the return structure (untrusted-data envelope). 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.

    Conciseness4/5

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

    The description is fairly long but well-structured: a concise purpose sentence, then example, then details. Every sentence adds value, though it could be slightly more trimmed. Still, it avoids redundancy and is 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?

    Despite no output schema, the description explains the return format (result, calledEndpoints, stdout/stderr in untrusted-data envelope), mentions blocked endpoints, and provides sufficient context for a read tool. No major gaps.

    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?

    Input schema has one parameter 'code' with minimal description. The description adds significant meaning: an example async function, the full type signature of the `api` object, and details on return values. This greatly enriches the agent's understanding of how to write the code.

    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 'Read data from the Logflare Management API by writing JavaScript' and specifies 'Only GET requests are allowed', providing a specific verb, resource, and method restriction. It distinguishes from sibling execute_write by noting that execute_write should be used for state changes.

    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 explicitly says 'use execute_write for anything that changes state' and advises 'Use the search tool first to find paths'. This gives clear when-to-use and when-not-to-use guidance, along with alternatives.

    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?

    Annotations already set destructiveHint=true and readOnlyHint=false. Description adds valuable context: the tool performs mutation (POST/PUT/PATCH/DELETE), explains the return envelope, and lists blocked endpoints. 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 purpose statement, usage guideline, example, and detailed param explanation. Slightly verbose but each section adds value. Could be tightened by removing redundant details like the full api definition in scope.

    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?

    For a single-parameter tool with no output schema, the description covers input (code format), API usage, return format, and security restrictions. Addresses all key aspects an agent needs to know.

    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 describes the 'code' parameter minimally. Description provides a full example, the api object definition, and details on how to structure requests, adding significant 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?

    Clearly states it calls the Logflare Management API with state-changing requests (POST/PUT/PATCH/DELETE). Distinguishes from sibling execute_read by specifying write vs read.

    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 tells when to use this tool ('for state-changing requests') and when to use the alternative ('Use execute_read for plain reads'). Warns about blocked endpoints (access-tokens, backends, teams) to prevent misuse.

    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?

    Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context: code runs in a network-isolated sandbox, output over 100k chars is truncated, and the function is async returning a value. 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?

    The description is well-structured: concise first line, then detailed interface, usage note, and examples. Every sentence adds value. It is appropriately sized for the complexity of the tool.

    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?

    The description covers all necessary aspects: sandbox, input specification, output truncation, and usage context. No output schema exists, but the description implicitly informs about return values. Complete for a tool of this complexity.

    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?

    The input schema describes one parameter 'code' with a basic description. The description significantly enhances meaning by providing the full TypeScript interface for `spec`, examples, and execution constraints. This far exceeds the schema coverage (100% 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?

    The description clearly states the tool's purpose: 'Search the Logflare Management API's OpenAPI spec by writing JavaScript.' It specifies the verb (search) and the resource (API spec), effectively distinguishing it from sibling tools like execute_read and execute_write.

    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 explicitly tells when to use the tool: 'Use this to find endpoints and their parameters/request bodies before calling execute_read/execute_write.' It provides a clear context and examples, guiding the agent on appropriate usage.

    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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