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LucasSantana-Dev

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by LucasSantana-Dev
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Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct scopes: rag_query searches broadly across code, docs, and commits, while search_knowledge targets only durable knowledge artifacts. Their descriptions explicitly state when to use each, eliminating ambiguity.

    Naming Consistency4/5

    Both names use lowercase snake_case and are descriptive, but the pattern differs: 'rag_query' places the technology prefix before the verb, while 'search_knowledge' follows a verb-noun structure. This minor inconsistency doesn't cause confusion, but a unified pattern like 'search_all' and 'search_knowledge' would be cleaner.

    Tool Count3/5

    With only two tools, the server feels minimally scoped, but for a focused search/retrieval service this is arguably sufficient. The two tools complement each other well without redundancy, though a few more specialized search options could justify a higher score.

    Completeness4/5

    The two tools cover broad and knowledge-specific search, including source code, commits, notes, and decisions. Minor gaps exist such as lacking a tool to retrieve a specific document by ID or list available sources, but core retrieval needs are met.

  • Average 4.2/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 12 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.

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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

  • Behavior4/5

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

    With no annotations, the description carries the burden. It discloses hybrid search, auto-scoping based on cwd, and result format (path:line + symbol + repo citations). It implies a read-only operation but does not explicitly state it, nor does it mention side effects or permissions. Still, the provided behavior detail goes beyond a minimal description.

    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 concise sentences that are front-loaded with the core action and followed by scoping/usage details. Every phrase adds value, with no redundant fluff. Well-structured for an LLM to quickly grasp.

    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 tool's complexity (5 params, no output schema, no annotations), the description covers the search scope, result format, scoping behavior, and an alternative use case. It is complete enough for an agent to invoke the tool correctly, though it omits explicit read-only confirmation and any potential rate limits.

    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?

    Schema coverage is 60%, and the description adds meaningful semantics for scope_repos (auto-scoping and disabling with ['all']) and references scope_types from sources.yaml. It also explains cwd drives auto-scoping. However, query and top are left to the schema without additional context, so it does not fully compensate for all parameters.

    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 it performs hybrid semantic + BM25 search over a specific corpus (notes, docs, repo docs, commits, transcripts, code) and returns top-K chunks with citations. It uses a specific verb ('search') and resource ('configured corpus'), but does not explicitly contrast with sibling search_knowledge. However, it differentiates from grep, giving some distinction.

    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 says 'Use instead of grep for fuzzy or cross-file recall,' providing an alternative use case. It also explains auto-scoping behavior and how to disable it with scope_repos=['all']. However, it does not mention when to prefer search_knowledge, leaving a minor gap.

    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?

    With no annotations, the description carries the full behavioral burden. It discloses cross-project behavior (no cwd auto-scoping), the semantic nature of the search, and the exclusion of source code/git commits. This gives the agent important expectations about scope and limitations, though it does not mention result format or potential side effects (which are likely none).

    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 compact, front-loaded with the primary purpose, and every clause adds value — defining content types, scope, usage, and alternative in three 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?

    The description covers what the tool searches, what it excludes, cross-project behavior, and when to use it versus the sibling. The only missing piece is the output/return format, but given the tool's simplicity and no output schema, the description is still quite complete.

    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?

    The schema has 0% description coverage, so the description must explain the parameters. It only implicitly addresses 'query' through examples ('is there a note about X') and completely omits 'top', leaving its meaning and constraints undocumented. This is a significant 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 clearly states 'Semantic search over the knowledge layer' and enumerates specific content types (memory notes, ADRs, plans, etc.), explicitly excluding source code/git commits. It also distinguishes from the sibling tool rag_query, making the tool's 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 Guidelines5/5

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

    It provides explicit when-to-use examples ('what did we decide / is there a note about X / did we hit this before') and an explicit alternative ('For source-code or git-commit recall, use rag_query instead'), clearly guiding selection.

    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 there are no obvious security issues.
  • Evaluate tool definition quality.

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