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maschmann

mcp-context-memory

by maschmann
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Python
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Server Quality Checklist

58%
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  • Latest release: v0.3.1

  • Disambiguation5/5

    Each tool has a clear, distinct purpose: index_project builds the semantic index, search_context queries it, and remember_decision stores explicit architectural notes. There is no functional overlap between these three operations.

    Naming Consistency5/5

    All tool names follow the same verb_noun pattern: index_project, search_context, remember_decision. The verbs are concise and the nouns accurately describe the target resource, making the API predictable and easy to navigate.

    Tool Count5/5

    With only 3 tools, the server is tightly scoped to its stated purpose of context memory. Each tool covers a distinct fundamental operation (index, search, remember), and there is no bloat or redundancy.

    Completeness4/5

    The tool set covers the core lifecycle of context memory: ingest (index_project), query (search_context), and explicit knowledge persistence (remember_decision). Minor gaps include lack of delete/update for decisions and no re-indexing mechanism, but these are not critical for the primary workflow.

  • Average 3.9/5 across 3 of 3 tools scored. Lowest: 3.3/5.

    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

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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, the description carries full burden. It discloses the tool's action (saving notes) but does not mention persistence behavior, whether it overwrites existing decisions, or any side effects. No annotation contradiction detected.

    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 concise and well-structured, with a lead sentence and clear arg list. No fluff or redundancy.

    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 the simple two-string-parameter form, the description covers the essential purpose and params. However, it omits behavioral details (e.g., how decisions are stored or retrieved) and does not provide usage context relative to siblings, leaving minor gaps.

    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 schema itself has 0% description coverage, but the tool description compensates by defining both params: topic as a short category and context as detailed reasoning. This gives meaningful semantic guidance beyond the raw string type.

    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 tool saves manual architectural notes or 'Why' decisions, using the verb 'save' with a specific resource. It distinguishes itself from sibling tools (index_project, search_context) by its save/remember role, though it doesn't explicitly name alternatives.

    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 use for recording architectural decisions but provides no explicit guidance on when to use it instead of search_context or index_project. No when-not-to-use conditions are given.

    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 transparency burden. It discloses important behavior like respecting .gitignore and using AST-based decomposition, but it does not explain side effects, persistence, repeatability, or whether the operation modifies files.

    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 concise sentences. The first delivers the core action and scope; the second handles the parameter. There is no redundant text or unnecessary detail.

    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?

    Since an output schema exists, return-value details are not required. The description covers the input, behavior (.gitignore, AST), and destination ('code_semantics'), which is largely complete for a one-parameter indexing tool, though edge cases and sibling relationships are not discussed.

    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?

    The schema has no property description, but the Args section clearly defines 'path' as 'The absolute or relative path of the directory to index' and notes the default. This provides complete semantic meaning for the single parameter, going well beyond the bare 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 uses a specific verb ('scans') and resource ('directory') while detailing the decomposition into class definitions, methods, and HTML/CSS structures. It clearly differentiates from sibling tools like 'search_context' and 'remember_decision' by describing an indexing operation.

    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 the tool is for indexing a project to build 'code_semantics', but it does not explicitly state when to use it versus searching or remembering decisions. There are no exclusions or alternative tool references.

    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 must disclose behaviors. It states what the tool searches, but does not explicitly mention that it is read-only or non-destructive. The verb 'search' implies a read operation, but this is not explicitly confirmed, and no side effects or limitations are disclosed.

    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 very brief: one sentence plus a parameter argument. It is front-loaded with the core purpose and contains no filler.

    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 single-parameter search tool, the description covers the essential purpose and parameter semantics. An output schema exists, so return values are handled elsewhere. It lacks usage examples or explicit limitations, but is largely complete for the tool's complexity.

    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 schema description coverage is 0%, so the description compensates by explaining the query parameter: 'The search query to find relevant context.' This adds meaning beyond the bare string type, though it is minimal and lacks examples or nuances about matching behavior.

    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 'unified search' and specifies the resources: 'code structures (AST nodes)' and 'past project decisions'. This clearly distinguishes it from sibling tools (index_project, remember_decision) which are about writing/storing rather than searching.

    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 implies usage context: it is the retrieval complement to index_project and remember_decision, as it searches across both types of data. However, it does not explicitly state when not to use this tool or mention 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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