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sdc-ren

localmem

by sdc-ren
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Python
Local

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    memory_recall and memory_add serve clearly distinct purposes: one reads memory, the other writes it. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with 'memory' prefix: memory_recall and memory_add. The naming is perfectly predictable.

    Tool Count3/5

    With only 2 tools, the server is at the bare minimum for a read/write memory store. It feels thin, but the core operations are present.

    Completeness3/5

    The server supports add and recall but lacks update/delete operations for memory entries. This is a notable gap for correcting stale facts or removing obsolete memories in a persistent store.

  • Average 4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 25 commits in the last 12 weeks
    • No stable releases found
    • 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.

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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

  • Behavior3/5

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

    With no annotations, the description must carry the transparency burden. It indicates a read operation via 'Search' but does not explicitly state read-only behavior, side effects, or permissions. The phrase 'persistent cross-session memory' adds context, but more detail on what happens during a search would improve 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?

    The description is a single, front-loaded sentence with no wasted words. It immediately states the action and then gives the triggering condition, making it highly concise and easy to parse.

    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 search tool, the description covers the core purpose and when to use it. The output schema exists to explain return values, so that burden is covered. It could mention result behavior or edge cases, but the current description is reasonably complete for an agent to invoke correctly.

    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 needed to compensate for parameter meanings, but it provides none. The parameter names (query, k, workspace) offer some inference, but the description does not explain their roles, such as k controlling result count or workspace filtering scope. This is a clear gap.

    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 uses a specific verb ('Search') and resource ('the user's persistent cross-session memory'), clearly explaining what the tool does. It also distinguishes from the sibling tool memory_add by focusing on recall rather than addition, making the purpose unambiguous.

    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 explicit context for when to use this tool: 'Call before answering questions about project history, past decisions, or user preferences.' This is clear and actionable, though it does not explicitly mention alternatives or exclusions, so it falls just short of a 5.

    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?

    With no annotations, the description carries the full burden. It discloses that memories are durable and persistent, and that search is lexical, which is useful. But it doesn't explain side effects, how supersedes works, or any authorization requirements, leaving notable 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?

    Three sentences, front-loaded with the primary action, then usage guidance and parameter tips. Every sentence earns its place with no fluff.

    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 memory-add tool with 6 optional params and an output schema, it covers the main use case, persistence, keywords, and content formatting. It leaves out semantics for source, workspace, and supersedes, but is otherwise quite complete for typical agent scenarios.

    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 0%, so the description must compensate. It explains kind='lesson', content as one line, and keywords with specific examples. However, source, supersedes, and workspace are not touched, so three of six parameters remain undocumented.

    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 opens with 'Save a durable fact, decision, or lesson to the user's persistent memory', using a specific verb and resource. This clearly distinguishes it from the sibling tool memory_recall, which would retrieve 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?

    It explicitly states when to call: 'Call when you learn something worth remembering across sessions.' It also provides concrete formatting guidance for bug lessons. However, it doesn't mention exclusions or compare to memory_recall, so it falls short of a 5.

    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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  • 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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