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jsiksn

Mnemosure

by jsiksn
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool serves a distinct purpose: listing memories, answering questions from memories, and storing new memories. There is no functional overlap.

    Naming Consistency5/5

    All tool names follow a consistent verb or verb_noun pattern in lowercase with underscores: list_memories, recall, remember.

    Tool Count4/5

    Three tools cover the core memory operations (list, query, store) well, but a few more (e.g., delete, update) might be expected for full lifecycle management.

    Completeness3/5

    The tool set lacks explicit delete and update operations. While remember can supersede old memories, there is no way to independently modify or remove memories.

  • Average 4/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 27 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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    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

  • Behavior4/5

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

    The description explains the default filtering by valid memories and describes the output format, providing transparency beyond the missing annotations. However, it does not explicitly state that the operation is read-only or discuss side effects, but this is implied.

    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 with no wasted words: a clear initial sentence, a parameter explanation, and an output format outline. Every sentence serves a purpose and is front-loaded.

    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?

    The description covers the tool's purpose, parameter, and output structure adequately for a simple list tool. It lacks details on pagination or limits, but the presence of an output schema mitigates this. Siblings exist but are not addressed.

    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 input schema has 0% description coverage, so the description must compensate. It clearly explains the 'include_superseded' parameter with its effect ('also include old memories that have been superseded'), adding meaningful context beyond the schema's title and default.

    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 action ('List the memory warehouse') and specifies default behavior (only currently-valid memories). However, it does not distinguish this tool from its siblings 'recall' and 'remember', which could cause confusion.

    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 the sibling tools 'recall' and 'remember'. The description does not include when-not-to-use or alternative contexts.

    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?

    No annotations, so description fully covers behavior: says 'not in the record' for unknowns, cites sources, corrects superseded facts, and provides output structure with confidence levels.

    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?

    Concise: three sentences conveying purpose, behavior, and output format without redundancy. Front-loaded with main purpose.

    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?

    Covers input parameter, output structure, and behavioral traits. Lacks mention of limitations like memory size or rate limits, but sufficient for a simple retrieval 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?

    Schema has 0% coverage for the 'query' parameter. Description adds meaning: 'the question (any language — the answer follows the question's language)', compensating well.

    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 the tool answers questions grounded in stored memories, with specific verb and resource. Distinguishes from siblings: list_memories (listing) and remember (storing).

    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?

    Implies usage by describing behavior (answers questions, handles unknowns, corrects superseded facts), but no explicit when-to-use/when-not-to-use or alternative comparisons.

    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?

    Since no annotations are provided, the description carries the full burden. It discloses automatic linking of supersedes/because and mentions the return format, which goes beyond just the function name. However, it does not describe idempotency or side effects beyond linking.

    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 moderately concise, with a clear structure separating purpose, parameter details, and return format. A few sentences could be more tightly integrated, but overall it is well-organized.

    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 has 3 parameters, no output schema (but description includes return format), and sibling tools listed, the description provides enough detail for correct use. Minor improvement would be to explicitly compare with sibling tools.

    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%, so the description must compensate. It fully explains each parameter: session_text as raw text, date as 'YYYY-MM-DD' with default today, and title as a human-readable source citation. This adds significant meaning 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 verb 'extract' and the resource 'durable memories', and adds specificity about automatic linking when a decision is overridden. It distinguishes from sibling tools (list_memories, recall) by focusing on memory ingestion.

    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 implies when to use (to extract memories from conversation text) but does not explicitly state when not to use or compare with alternatives like list_memories or recall.

    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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