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Server Quality Checklist

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  • Latest release: v0.1.0

  • Disambiguation5/5

    The three tools have clearly distinct purposes: 'save' is for storing research, 'search' is for retrieving research, and 'stats' is for viewing personal metrics. There is no overlap in functionality, making it easy for an agent to select the correct tool for each task.

    Naming Consistency4/5

    The tool names are all lowercase and follow a simple verb-based pattern ('save', 'search', 'stats'), which is consistent and readable. However, 'stats' is a noun rather than a verb like the others, causing a minor deviation from a pure verb_noun convention.

    Tool Count5/5

    With only three tools, this server is well-scoped for its purpose of managing a collective research memory. Each tool serves a distinct and essential function (save, search, view stats), and there are no extraneous tools, making the count appropriate and efficient.

    Completeness4/5

    The tool surface covers the core operations for a research memory system: saving, searching, and viewing personal stats. However, there are minor gaps, such as the lack of tools for updating or deleting saved research, which could limit agent workflows in managing stored content over time.

  • Average 4/5 across 3 of 3 tools scored. Lowest: 3.4/5.

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

    • 0 of 1 community issues answered or closed 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 is passing
  • This repository is licensed under AGPL 3.0.

  • 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.

  • This repository includes a glama.json configuration file.

  • If you are the author, simply .

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    {
      "$schema": "https://glama.ai/mcp/schemas/server.json",
      "maintainers": [
        "your-github-username"
      ]
    }

    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

  • Behavior3/5

    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 indicates a read-only operation ('View') and specifies the type of data returned, but doesn't mention potential limitations like authentication needs, rate limits, or data freshness. This is adequate for a simple stats tool but lacks depth.

    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, efficient sentence that front-loads the purpose and lists key metrics without any wasted words. Every element earns its place by clarifying what the tool does.

    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 (0 parameters, no output schema, no annotations), the description is complete enough for basic understanding. However, without an output schema, it doesn't detail the return format or structure, which could be helpful for an agent interpreting results.

    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?

    The tool has 0 parameters, and schema description coverage is 100%, so there's no need for parameter details in the description. The baseline for 0 parameters is 4, as the description appropriately doesn't waste space on nonexistent parameters.

    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 purpose with a specific verb ('View') and resource ('personal wellread stats'), listing specific metrics like karma, savings, contributions, and network impact. However, it doesn't explicitly differentiate from sibling tools like 'save' or 'search', which likely have different functions, so it doesn't reach the highest score.

    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 provides no guidance on when to use this tool versus alternatives like 'save' or 'search'. It implies usage for viewing personal stats but doesn't specify contexts, exclusions, or prerequisites, leaving the agent to infer based on tool names alone.

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

  • Behavior4/5

    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 effectively describes critical behavioral traits: the tool must be called first and alone (sequential execution constraint), results contain instructions that must be followed exactly, and it should be skipped for chitchat. This provides substantial operational context beyond basic functionality.

    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 exceptionally concise and well-structured. Every sentence earns its place: the first states the purpose, the second provides critical usage guidelines, and the third specifies how to handle results. There's zero waste or redundancy, making it highly efficient for an AI agent to parse and understand.

    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 tool's complexity (5 parameters, no output schema, no annotations), the description provides strong contextual completeness. It covers purpose, usage constraints, and behavioral expectations. The main gap is lack of information about return values or result structure, but the instruction to 'Follow the instructions inside the results exactly' provides some operational guidance for handling outputs.

    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 description coverage is 100%, providing complete parameter documentation. The description adds no specific parameter semantics beyond what's in the schema. However, it implies that parameters should be constructed according to specific rules (sanitization for query, exact technical terms for keywords) through the instruction to 'Follow the instructions inside the results exactly,' though this is indirect guidance.

    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 purpose: 'Search collective research memory.' This is a specific verb+resource combination that distinguishes it from sibling tools like 'save' and 'stats.' However, it doesn't explicitly differentiate from potential external alternatives like web searches, though it implies this through usage guidelines.

    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, detailed usage guidelines: 'Call FIRST and ALONE (no parallel tools) before any web search or implementation. Skip for chitchat.' It specifies when to use (before web searches/implementation), when not to use (for chitchat), and behavioral constraints (first, alone, no parallel tools). This is comprehensive guidance for an AI agent.

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

  • Behavior4/5

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

    With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: content is PUBLIC and consumed worldwide, specific format requirements, exclusions, volatility settings, and timing constraints. It doesn't mention rate limits or authentication needs, but covers most critical behavioral aspects for this type of tool.

    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 efficiently structured with clear sections: purpose, timing, content rules, exclusions, volatility, and format example. Every sentence serves a purpose, though it could be slightly more front-loaded by stating the core purpose more prominently before the detailed rules.

    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?

    For a complex 9-parameter tool with no annotations and no output schema, the description provides substantial context about behavioral expectations, content rules, and usage timing. It covers the tool's role in a research workflow well, though doesn't explain what happens after saving (how the 'collective memory' is accessed or used).

    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 description coverage is 100%, so the schema already documents all 9 parameters thoroughly. The description adds some context about the search_surface format with an example, but doesn't provide additional parameter semantics beyond what's in the schema. Baseline 3 is appropriate when schema does the heavy lifting.

    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: 'Save research to collective memory' with specific guidance on content format ('Dense structured notes — no tutorials') and language requirements ('ALWAYS English'). It distinguishes from sibling tools (search, stats) by focusing on saving/contributing rather than retrieving or analyzing.

    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 usage timing ('Call directly BEFORE responding to the user, after any live research') and context ('web search, URL fetch, context7'). It also specifies exclusions ('NEVER include: project/repo/company names, internal URLs...') and volatility guidelines, giving comprehensive when-to-use guidance.

    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:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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