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agent-memory

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Server Quality Checklist

67%
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  • Latest release: v0.5.1

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: fetch_context retrieves context, propose_update proposes edits, and status reports health. There is no overlap or ambiguity.

    Naming Consistency5/5

    All tool names follow the pattern memory.<verb>_<noun> using snake_case, ensuring consistent and predictable naming.

    Tool Count5/5

    3 tools is well-scoped for a focused memory management server, covering reading, writing, and monitoring without being too few or too many.

    Completeness4/5

    The tool surface covers key operations (read, propose, status), but the apply/reject actions for proposals are only available via CLI, not as MCP tools, which is a minor gap.

  • Average 4.5/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 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 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 provided, so description carries full burden. It discloses key behaviors: budgeted, ranked, Markdown output, bootstrap pack for empty query. Would benefit from mentioning auth or failure modes, but is adequate.

    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, no waste. Front-loaded with purpose, followed by usage guidance. Efficient and direct.

    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 5 parameters and output schema exists, description covers purpose, usage, and parameter behavior. Lacks error handling details but is sufficient for the tool's role.

    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?

    Input schema covers all 5 parameters with descriptions (100% coverage). Description adds context like 'empty returns bootstrap pack' for query and 'approximate character budget' for budget, but mostly reinforces schema info.

    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 returns a budgeted, ranked Markdown context pack from .agent-memory/ files. Distinguishes from siblings (propose_update, status) which are different operations.

    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?

    Explicitly advises calling before reading source files manually, and explains empty query returns bootstrap pack. Lacks explicit when-not-to-use or alternatives, but guidance is clear.

    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?

    No annotations provided, so description carries full burden. It describes validation, secret scanning, provenance requirements, immediate vs staged application, and rejection handling. Very transparent.

    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?

    Description is dense but well-structured, covering all key aspects without verbosity. Slightly longer than minimal but earns its content.

    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 complexity (nested operations, multiple intents, review workflow) and presence of output schema, description fully covers behavioral aspects and lifecycle. No gaps.

    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 baseline is 3. Description adds context on intent categories and staging but does not significantly enhance parameter meaning beyond 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 proposes structured edits to .agent-memory/ files, with specific verbs and resource. Distinguished from siblings memory.fetch_context and memory.status, which are read-only and status checks respectively.

    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?

    Explains when proposals are applied immediately vs staged for human review, but does not explicitly state when to use this tool vs alternatives. However, given siblings, context is clear.

    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?

    No annotations provided, so description fully discloses read-only behavior: 'Read-only; never modifies any file.' Also details what information is reported, giving full transparency.

    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: first lists what is reported, second gives purpose. Front-loaded with main functionality, no redundant or filler content.

    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 annotations or output schema in the definition, description thoroughly covers the tool's purpose, behavior, and usage context. Output schema exists, so return value details are not needed from description.

    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?

    No parameters; schema coverage is 100% so no parameter documentation needed. Baseline score of 4 is appropriate as description adds no param info, but none is required.

    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 it reports memory health and metadata, listing specific items (file counts, sizes, pending proposals). Differentiates from siblings memory.fetch_context and memory.propose_update, which handle context and updates respectively.

    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 call: 'to decide whether memory needs maintenance...' and 'before proposing further updates.' Also indicates read-only nature, guiding safe 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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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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