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Nimo1987

Harness Research MCP

by Nimo1987

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v2.0.0

  • Disambiguation5/5

    Each tool has a distinct purpose: harness_research initiates a deep research session, harness_search provides quick results, and harness_status checks progress. No overlap in functionality.

    Naming Consistency5/5

    All tool names follow the consistent pattern 'harness_<verb>' using underscores, with predictable conventions.

    Tool Count5/5

    Three tools cover the essential workflow of starting research, quick searching, and status polling. The count is well-scoped for the server's purpose.

    Completeness4/5

    The tool set covers the core research workflow (start, search, check progress) but lacks a cancel or delete operation for tasks, which is a minor gap.

  • Average 4.7/5 across 3 of 3 tools scored.

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

    • 1 of 1 community issues answered or closed 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 Apache 2.0.

  • This repository includes a README.md file.

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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 are present. The description discloses that results are structured and that the tool completes in seconds. It does not detail auth requirements, rate limits, or error behavior, but for a straightforward search tool, this level of transparency is sufficient.

    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 two sentences long, front-loaded with the key purpose, and contains no superfluous information. Every word contributes to understanding the tool's function.

    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?

    Given the simple parameters and lack of output schema, the description adequately covers the tool's behavior. It could be improved by briefly mentioning the output format, but the current description provides necessary context for selection and invocation.

    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 parameter descriptions are present. The description adds value by explaining the default behavior for sources ('all available') and limit (5), and by contextualizing the search as quick and multi-source. This goes beyond the schema's basic descriptions.

    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 performs a quick multi-source search and lists the specific sources (Tavily, Brave, arXiv, PubMed). It explicitly contrasts with generating a full report, distinguishing it from the sibling tool 'harness_research'.

    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 phrase 'without generating a full report' implies this tool is for quick searches when a full report is not needed. While it does not explicitly state when not to use it, the sibling names suggest 'harness_research' is for comprehensive reports, providing adequate guidance.

    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, the description fully discloses that the tool returns immediately (non-blocking), runs in background, and uses affordable models ($0.01/run). It also mentions PDF availability limitation (macOS only). All behavioral traits are transparently communicated.

    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 with a clear heading, an IMPORTANT note, and a numbered workflow. Each sentence is purposeful, no redundant text, efficiently conveying essential information in a few paragraphs.

    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 the tool's complexity (async, 5 parameters, multiple outputs), the description covers all key aspects: async behavior, polling, output format limitations, and cost. No output schema exists, but the description explains that harness_status returns file paths, completing the context.

    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 parameters are well-described structurally. The description adds value by explaining defaults (e.g., provider='kimi', model='kimi-k2.5', formats=['html','docx']) and the purpose of the tool, though the schema itself already covers basic parameter 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 explicitly states it 'start[s] a deep research session' with multi-source search and report generation. It lists sources (Tavily/Brave/arXiv/PubMed/Tushare) and output formats (HTML/DOCX/PDF), clearly distinguishing it from siblings like harness_search (likely simple search) and harness_status (status polling).

    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 provides explicit step-by-step workflow: call this tool, get task_id, poll harness_status every 30-60s until completion. It warns against idle waiting and notes the ~8-12 minute runtime, giving clear when-to-use and how-to-use guidance.

    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, the description fully discloses behavior: polling mechanism, status meanings, and inclusion of output file paths or error messages. It also covers the fallback of listing all tasks.

    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?

    Extremely concise: one sentence for purpose, bullet points for statuses, and a closing note on missing task_id. Every sentence adds value without redundancy.

    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 polling tool with no output schema, the description covers all essential aspects: when to use, how to poll, status interpretations, and listing fallback. No additional context is needed.

    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 already describes task_id as optional and its omission lists all tasks. Description adds minimal extra context (the polling behavior and status outcomes), but since schema coverage is 100%, baseline is 3; slight improvement justifies 4.

    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 checks progress of a research task started by harness_research, distinguishing it from the sibling tools harness_research (starts tasks) and harness_search.

    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 states when to use (after harness_research), polling frequency (every 30-60 seconds), and what to do based on status (keep polling, retrieve outputs, or handle error). Also mentions listing all tasks if no task_id.

    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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Glama performs regular codebase and documentation scans to:

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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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