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markchiang

gpt-researcher-mcp

by markchiang
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.5

  • Disambiguation5/5

    The two tools, add-note and get_report, have completely distinct purposes with no overlap. An agent can easily tell them apart based on their names and descriptions.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern using hyphens (add-note, get-report). This is a clear and predictable naming convention.

    Tool Count3/5

    With only two tools, the server feels thin and borderline under-scoped for a research-focused MCP server. Two tools fall into the lower end of acceptable range but do not feel entirely insufficient.

    Completeness2/5

    The server lacks core operations for a research workflow, such as initiating research, listing past reports, or managing notes beyond adding. Significant gaps exist that would likely cause agent failures in practical use.

  • Average 2.8/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 0 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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  ]
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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 only says 'Add a new note' and provides no details on side effects, permissions, return values, or error behavior.

    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 a single short sentence with no wasted words, making it concise and front-loaded. However, it is under-specified for the tool's complexity, though this is largely a completeness issue rather than a conciseness issue.

    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?

    Given the lack of annotations, output schema, and parameter descriptions, a five-word description is insufficient for an agent to invoke the tool correctly. The meaning of the parameters and expected return behavior remain ambiguous.

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

    Parameters1/5

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

    The input schema has zero description coverage for the two required parameters ('name' and 'content'), and the description does not explain their meaning or format. The agent cannot infer what 'name' or 'content' represent.

    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 uses a specific verb ('Add') and a specific resource ('note'), clearly stating the tool's function. It differentiates itself from the sibling 'get_report', which is a read operation.

    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. The description only states the action without any context about when to invoke it or when to choose a sibling tool.

    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?

    No annotations are provided, so the description carries the full burden for behavioral disclosure. It only says 'get' a report, which implies a read operation, but it does not disclose potential side effects, permission requirements, rate limits, or what the return value contains. The lack of any additional behavioral context makes this inadequate for a tool with zero annotation coverage.

    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 directly states the tool's purpose. Every word contributes to the core message, with no fluff or redundancy. It is front-loaded and efficiently sized for its minimal content.

    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?

    Given there is no output schema and no annotations, the description should explain what constitutes a report, what the return format is, and how the parameters affect the result. It does none of these. The tool is simple (2 parameters), but the description leaves significant gaps in understanding the tool's behavior and expected inputs.

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

    Parameters2/5

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

    Schema description coverage is 0%, and the input schema only gives parameter names and types. The description adds little beyond restating the parameter names: 'based on a query and report type' provides minimal context but no detail on valid formats, allowed values, dependencies, or defaults. It does not compensate for the missing schema descriptions.

    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 gets a report, with a specific verb and resource, and mentions the two inputs (query and report type). It does not explicitly differentiate from the only sibling 'add-note', but the difference is obvious: one adds notes, the other gets reports. This is clear enough for a 4 but not a 5 due to lack of explicit sibling distinction.

    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 guidance on when to use this tool versus alternatives, no exclusions, and no context about prerequisite conditions or limitations. It simply states what it does, leaving the agent no direction on when it is the appropriate choice over other tools.

    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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  • Evaluate tool definition quality.

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