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squatch-c-c

squatch-harbor-mcp

by squatch-c-c
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Python
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Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool serves a unique purpose: ingest indexes files, search queries the index, and stats reports index metadata. No functional overlap.

    Naming Consistency5/5

    All tools follow a consistent 'harbor_verb' pattern (harbor_ingest, harbor_search, harbor_stats), using snake_case and clear verbs.

    Tool Count5/5

    With 3 tools, the set is minimal but perfectly scoped for the domain: index creation, querying, and status checking. No unnecessary tools.

    Completeness4/5

    The tool surface covers the full lifecycle: ingest (create/update), search (read), and stats (monitor). A tool to delete or clear the index is not provided but can be achieved via rebuild flag, so minor gap.

  • 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
    • 1 commit 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

  • 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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    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?

    With no annotations, the description bears full burden. It reasonably discloses the tool's behavior: it returns top matches with session, date, role, and snippet. It mentions the dependency on harbor_ingest, but could further clarify what happens if no data is indexed (e.g., empty results).

    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 at two sentences, front-loading the purpose. Every sentence adds value. Could be slightly more structured (e.g., separate prerequisite note), but overall efficient.

    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 simple tool with an output schema, the description covers the main functionality and return fields. It does not discuss edge cases, pagination, or ordering of results, but is adequate for a search tool with clear scope.

    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 coverage is 0%, so the description should fully explain parameters. However, it does not elaborate on the 'query' or 'k' parameters beyond their names. The description mentions 'top matches' which weakly hints at 'k', but provides no guidance on format or behavior.

    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 searches indexed Claude history for past work, specifying the verb 'search' and the resource 'indexed Claude history'. It mentions returning top matches with details, but does not explicitly differentiate from the sibling tool 'harbor_stats', which could be another retrieval tool.

    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 usage context: use to find past work by query. It includes a crucial prerequisite: 'Run harbor_ingest first', which guides the agent on correct sequencing. However, it does not specify when not to use this tool or suggest alternatives among siblings.

    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, the description must disclose behavioral traits. It mentions the tool may indicate 'not built yet', which is a useful conditional behavior, but lacks details on caching, performance implications, or authorization needs. Minimal disclosure.

    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 sentence, concise and front-loaded with the key information. Every word contributes to understanding the tool's output.

    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?

    While the description covers the main purpose, it lacks details on the return format or structure. Since no output schema exists, more information about the response shape would improve completeness.

    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?

    No parameters exist, and schema coverage is 100%. Baseline for zero parameters is 4. Description adds no param info, but no missing information since there are none.

    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 tool returns size metrics of the harbor index: message count, file count, date range, or indicates if not built. It distinguishes from sibling tools harbor_ingest (ingestion) and harbor_search (search).

    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 usage for checking index status and size, but does not explicitly provide when to use or alternatives. No exclusions or when-not-to-use guidance is given.

    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?

    Without annotations, the description carries full burden. It explains incremental default, rebuild option, and default directory. Missing details on side effects or whether rebuild destroys old index, but 'start fresh' is clear.

    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 that are front-loaded with purpose and follow with key details. No fluff or redundancy.

    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?

    Description is complete for a simple tool without output schema. It covers purpose, behavior, and defaults. Could mention that results can be searched later, but siblings imply that.

    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 has 0% description coverage, so the description adds essential meaning. It explains the default for sessions_dir and the effect of rebuild=true, covering both parameters effectively.

    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 that the tool indexes local Claude session transcripts for searching. It distinguishes itself from sibling tools like harbor_search and harbor_stats by focusing on ingestion.

    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 explains when to use the tool (indexing transcripts) and provides details on incremental vs. rebuild behavior. However, it does not explicitly state when not to use it or compare directly to siblings.

    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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Glama performs regular codebase and documentation scans to:

  • 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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