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allenc84

claude-memory

by allenc84
OverviewSchemaRelated ServersScoreDiscussions
Python
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.1

  • Disambiguation5/5

    Each tool has a clearly distinct purpose. Overlapping functions like list_memories, search_memory, and get_related are well-differentiated by their descriptions (recent vs. semantic vs. related).

    Naming Consistency4/5

    Most tools follow a verb_noun pattern (e.g., log_assessment, resolve_assessment), but 'memory_stats' breaks the pattern as a noun_noun, and 'consolidate' lacks a noun. Overall still readable.

    Tool Count5/5

    12 tools is well within the optimal 3-15 range. The toolset covers memory management, assessment tracking, calibration, and system maintenance without unnecessary tools.

    Completeness3/5

    Core workflows are covered (create, search, assess, resolve), but missing basic CRUD operations like update/delete for memories or a get_memory_by_id tool. Assessment listing only shows pending ones, omitting resolved history.

  • Average 3.8/5 across 12 of 12 tools scored. Lowest: 2.6/5.

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

    • 0 of 1 community issues answered or closed in the last 6 months
    • 20 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.

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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?

    No annotations are provided, so the description carries the full burden. It mentions ranking by 'relevance × salience' but does not disclose read-only nature, required permissions, side effects, or rate limits. The phrase 'without being asked' is slightly misleading for an explicit search tool.

    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-loads the core purpose, and contains no extraneous information. Every sentence adds value.

    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 4 parameters, no output schema, and no annotations, the description provides purpose and ranking behavior but lacks output format details, error handling, and sufficient differentiation from sibling tools. It is adequate but has 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 coverage is 100% and parameter descriptions in the schema are detailed. The tool description adds no additional parameter-level information beyond the schema, so baseline score of 3 is appropriate.

    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 it searches the memory system by semantic similarity and provides a specific use case (start of topic-specific conversation). However, it does not explicitly differentiate from the sibling tool 'get_context_brief', which likely retrieves context in a different way.

    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 gives a clear usage context ('at the start of any topic-specific conversation') but lacks explicit when-not-to-use guidance or comparison with alternatives like 'get_context_brief' or 'save_memory'.

    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 carries the full burden. It correctly implies the tool is read-only (synthesizing memories) and mentions pulling memories via top_k parameter, but does not disclose edge cases (e.g., behavior on unknown topics) or potential side effects. This is adequate for a simple read query.

    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 with no fluff: first sentence defines the function, second sentence provides usage guidance. Every word earns its place.

    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 simplicity (two parameters, no output schema), the description explains the purpose and usage context well. It could mention output format, but not required since no output schema exists. Overall, it is nearly complete for an agent's decision-making.

    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. The description adds examples for the topic parameter but does not elaborate on top_k beyond what the schema provides (default value). The schema itself is clear, so the description adds marginal value.

    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 it synthesizes a brief on a topic covering known information, evolution of thinking, and open questions, with concrete examples like 'web onboarding tests' and 'stock portfolio'. This is specific and distinguishes from sibling tools like search_memory which retrieves raw memories.

    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 advises 'Use before deep-diving into any recurring topic', providing clear context and intent. While it does not explicitly exclude use cases or name alternatives, the guidance is sufficient for an agent to understand when to invoke this tool.

    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 carries the full burden. It does not disclose any behavioral traits such as persistence, overwrite behavior, or side effects beyond stating it saves a memory.

    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—two sentences with no wasted words. The purpose is front-loaded and each sentence contributes meaningful context.

    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 no output schema, the description covers the save action well but does not mention return value or confirmation. However, for a straightforward save operation, it is sufficiently complete.

    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 description coverage is 100%. The description adds value by specifying that content should be 'specific and self-contained' and gives examples for salience values, going beyond schema 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 explicitly states the tool saves a new memory and lists specific use cases (decisions, insights, feedback, etc.), clearly distinguishing it from sibling tools like search_memory and get_context_brief.

    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 provides clear guidance on when to use the tool (recording important conversation context), but does not explicitly mention when not to use it or alternatives.

    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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