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Server Quality Checklist

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  • Latest release: v0.1.1

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: storing, searching, counting, and deleting memories. No two tools overlap in functionality, so an agent can easily select the correct one.

    Naming Consistency5/5

    All tool names follow a verb_noun pattern in snake_case (store_memory, search_memories, count_memories, delete_memory). The slight plural/singular variation is negligible and does not break consistency.

    Tool Count5/5

    Four tools is an appropriate number for a focused memory server. Each tool serves a core operation without unnecessary bloat or missing essentials.

    Completeness4/5

    The set covers create, read (search and count), and delete operations for memories. An update tool is missing, but that is often handled by delete+store. The lack of a direct get-by-ID tool is a minor gap.

  • Average 4.1/5 across 4 of 4 tools scored. Lowest: 3.4/5.

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

    • No community issues in the last 6 months
    • 13 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 MIT License.

  • 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.

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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

  • Behavior2/5

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

    With no annotations, description carries full burden. It implies a read operation but omits details on error handling, rate limits, or behavior for missing agent/user pairs.

    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?

    Two sentences plus Args list, front-loaded with the main purpose. No redundant information.

    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 simple count tool with output schema, description covers core purpose. Could mention return format, but output schema likely handles that.

    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 has 0% description coverage; description adds 'Agent identifier' and 'User identifier', clarifying the purpose beyond schema titles. Minimal but adequate.

    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?

    Description clearly states verb 'Return', resource 'total number of stored memories', and scope 'for an agent/user pair'. Distinct from sibling tools (delete, search, store).

    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 on when to use count_memories versus alternatives like search_memories or store_memory. Lacks context or exclusions.

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

  • Behavior3/5

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

    The description explains the ranking mechanism and blend weight, but without annotations, it lacks details on side effects, authentication, rate limits, or behavior when no results are found. It adequately describes the core functionality.

    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 concise and well-structured with a docstring format. It front-loads the purpose and lists parameters clearly. Could be slightly more concise, but overall efficient.

    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 that an output schema exists, the description focuses adequately on input parameters. It covers required and optional parameters with defaults. Missing details like pagination or error handling, but sufficient for a search tool.

    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 description provides detailed explanations for all parameters, including the meaning of alpha (blend weight) and memory_type filter options. Schema coverage is 0%, so the description adds significant value 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?

    The description clearly states the tool searches memories using semantic similarity and recency decay, returning top-k ranked results with scores. It distinguishes itself from sibling tools like store_memory, delete_memory, and count_memories.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description explains the parameters and default values but does not provide explicit guidance on when to use this tool versus alternatives. No 'use when' or 'avoid when' instructions are given.

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

  • Behavior3/5

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

    No annotations are provided, so the description must carry the full behavioral disclosure. It mentions the ownership check and deletion, but does not describe idempotency, error states, or return values (though output schema exists).

    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 very concise with a clear structure: one-line summary followed by parameter descriptions. No wasted words.

    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 delete operation and presence of an output schema, the description is almost complete. It could mention idempotency or confirmation, but the existing details (ownership check, parameter sources) suffice.

    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 description coverage is 0%, but the description adds meaningful explanations: agent_id is 'Agent that owns the memory' and memory_id is 'UUID of the memory to delete (from store_memory or search_memories)'. This compensates fully.

    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 'Delete a specific memory by ID', which is a specific verb-resource pair. It distinguishes from sibling tools (store, search, count) by specifying deletion.

    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 gives context on usage by stating 'agents cannot delete each other's memories', implying a constraint. However, it does not explicitly state when to use this tool over 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?

    With no annotations provided, the description fully discloses key behaviors: local embedding via ONNX, persistence to Postgres, response format (memory ID), content length limit (8000 chars), memory type list, and importance range with usage hint.

    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 concise yet complete: a one-line summary followed by a clear bullet list of parameters. There is no fluff; each sentence adds value.

    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 presence of an output schema and the complexity (5 parameters, 3 required, defaults), the description covers essential behavioral and parameter info. It mentions the output but could be slightly enhanced by noting potential error conditions, though not required.

    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?

    Since schema description coverage is 0%, the description must compensate, and it does so thoroughly. It explains each parameter's purpose (agent_id, user_id, content with max chars), enum-like values for memory_type with defaults, and importance with range and recommendation.

    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 action ('Store a memory'), the subject ('for an agent/user pair'), and the outcome ('returns the memory ID'). It differentiates from sibling tools (count, delete, search) by being the store operation.

    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 details all parameters and defaults, providing implicit usage context (e.g., memory_type options, importance hint). However, it lacks explicit guidance on when to use this tool over alternatives like search_memories or delete_memory.

    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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