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akaiserg

MCP Memory Tracker

by akaiserg
OverviewSchemaRelated ServersScoreDiscussions
Python

Server Quality Checklist

50%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one saves memories and the other searches memories. There is no overlap or ambiguity between these operations, making it easy for an agent to select the correct tool based on the intended action.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (save_memory, search_memories) with clear, descriptive verbs. The naming is uniform and predictable, adhering to snake_case throughout without any deviations.

    Tool Count2/5

    With only two tools, the server feels thin for a memory tracker domain. While save and search are core operations, typical memory systems might also include update, delete, list, or clear functions. The count is too low for comprehensive coverage, limiting agent capabilities.

    Completeness2/5

    The tool surface is significantly incomplete for a memory tracker. It lacks essential operations like updating or deleting memories, listing all memories, or managing memory categories. This creates dead ends for agents trying to perform full lifecycle management of memories.

  • Average 2.8/5 across 2 of 2 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
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

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  • 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

  • Behavior2/5

    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 states the action is to 'Save' but doesn't mention whether this is a write operation, what permissions are needed, if it's idempotent, or what happens on success/failure. For a mutation tool with zero annotation coverage, this leaves significant behavioral gaps.

    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 extremely concise with a single sentence that directly states the tool's purpose. There's no wasted language or unnecessary elaboration, making it efficiently front-loaded and easy to parse.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given this is a mutation tool with no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It doesn't address behavioral aspects like side effects, error conditions, or return values, leaving the agent with insufficient context for reliable invocation.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    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 for the undocumented parameter. It mentions 'memory' but doesn't explain what constitutes a valid memory, its format, length constraints, or how it's stored. The description adds minimal semantic value beyond the parameter name itself.

    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 ('Save') and target resource ('a memory to the vector store'), providing a specific verb+resource combination. However, it doesn't differentiate from its sibling 'search_memories' beyond the obvious action difference, so it doesn't fully distinguish from alternatives.

    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 its sibling 'search_memories' or any other alternatives. It lacks context about prerequisites, appropriate scenarios, or exclusions, leaving the agent with minimal usage direction.

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

  • Behavior2/5

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

    With no annotations provided, the description carries full burden but offers minimal behavioral insight. It mentions searching a 'vector store' which hints at semantic matching, but doesn't disclose details like return format, pagination, error handling, or performance characteristics.

    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 with no wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given no annotations, no output schema, and low schema coverage, the description is incomplete. It lacks details on behavior, parameters, and expected results, making it inadequate for a tool that performs a non-trivial search operation.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    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 but adds little. It mentions 'query' but doesn't explain what constitutes a valid query (e.g., keywords, natural language), its format, or how matching works, leaving parameter meaning unclear.

    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 ('Search') and resource ('vector store for memories'), making the purpose understandable. It doesn't explicitly distinguish from the sibling 'save_memory', but the verb 'Search' versus 'save' provides implicit differentiation.

    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 or in what context. It mentions 'match the query' but doesn't specify scenarios, prerequisites, or exclusions, leaving usage ambiguous.

    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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