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zzallirog

weighted-compact

by zzallirog
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Python
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Server Quality Checklist

83%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.2.0-beta.3

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: compact_session compacts a source pair's session, search_pairs performs cosine search on embeddings, and substrate_info provides diagnostic info. No overlap in functionality.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case: compact_session, search_pairs, substrate_info. Predictable and easy to understand.

    Tool Count4/5

    With only 3 tools, the server feels minimal but well-scoped for its niche purpose. The tools cover the core actions (compact, search, info) and the count is reasonable given the specialized domain.

    Completeness3/5

    The tools cover the main operations for consuming compacted sessions, but lack tools to create or manage the underlying substrate (e.g., bootstrap, rem-pass mentioned as dependencies). This creates a notable gap for autonomous operation.

  • Average 4.5/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 24 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

  • 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.

  • This repository includes a glama.json configuration file.

  • If you are the author, simply .

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

    Then . Browse examples.

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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

  • Behavior5/5

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

    Discloses model loading/caching, required files, error handling (returns error dict instead of raising). Provides output format with preview lengths and score range. No annotations to contradict.

    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?

    Well-structured docstring covering purpose, output, prerequisites, error handling. Slightly verbose but informative, no wasted sentences.

    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 all essential aspects: purpose, parameters, output format, dependencies, error behavior. Given low parameter count and no annotations, description is complete enough for an agent.

    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?

    With 0% schema description coverage, the description adds minimal but sufficient meaning: query is text for cosine search, top_k controls count (default 10). Could be more explicit.

    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?

    Clearly states cosine-search of correction-text embeddings, returns top-K similar pairs. Distinct from sibling tools compact_session and substrate_info.

    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?

    Prerequisites (features.npz, sentence-transformers extra) are stated, and error handling behavior is described. Lacks explicit when-not-to-use or alternatives, but context is clear.

    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?

    Detailed behavioral description without annotations: hides source pair, ranks by chosen scorer, truncates by k_drop, error handling. Does not explicitly state side effects (e.g., read-only), but the description covers most behavioral traits.

    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?

    Well-structured with clear sections, but slightly verbose for a concise description. Could tighten wording while retaining all information.

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

    Completeness5/5

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

    Covers all aspects: purpose, parameters, behavior, return value structure, error handling. Output schema referenced but description explains keys; no gaps identified.

    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 coverage is 0%, but description compensates fully by explaining each parameter's role, constraints, and examples (e.g., k_drop defaults, ranker limitations, rem_decay requirement).

    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 states a specific verb ('Assemble a compacted-markdown view') and resource ('source pair's session'). It distinguishes from siblings by noting query-aware rankers belong in search_pairs.

    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?

    Clear context for when to use (compaction, hiding source pair) and alternatives (search_pairs for query-aware rankers). Lacks explicit when-not-to-use, but provides helpful limitations and prerequisites.

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

  • Behavior5/5

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

    With no annotations, the description fully describes behavior: returns a dict with specific fields, includes path, and is a cheap read. No side effects mentioned; appropriate for a diagnostic tool.

    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?

    Compact with three sentences, front-loaded purpose. Minor redundancy but overall efficient.

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

    Completeness5/5

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

    Given output schema exists, description adds usage context and lists key fields, making it complete for a zero-param 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?

    No parameters in schema, so no param info needed. Description adds value by explaining what the output contains, going beyond the schema's empty input.

    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 reports 'what's built and what isn't' as a cheap diagnostic, and lists specific return fields. It distinguishes from sibling tools (compact_session, search_pairs) by being a diagnostic info tool.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Explicit guidance: 'Use this on connect to know which other tools will succeed without paying their setup cost first.' Also notes it always returns a dict even when nothing is built.

    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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