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cxk280

locksmith

by cxk280
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Server Quality Checklist

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

  • Latest release: v0.1.1

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: analyze_migration scans migrations for risky lock operations, while explain_lock describes lock modes and blocking behavior. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern: analyze_migration and explain_lock. The naming is predictable and clearly reflects each tool's action and target.

    Tool Count3/5

    With only two tools, the server feels minimal but still coherent for its narrow domain of PostgreSQL lock safety. It is borderline thin, but the two tools cover the primary use case of analyzing migrations and understanding locks, so the count is acceptable.

    Completeness4/5

    The tool surface covers the full workflow of checking a migration for dangerous locks and providing educational support on lock modes. Minor gaps exist, such as no tool for inspecting live database locks or comparing lock compatibility, but the core purpose is well-served.

  • 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
    • 4 commits 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.

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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?

    There are no annotations, so the description carries the full burden. It discloses the core behavior (accepts fragments, explains blocking and acquisitions) but does not detail edge cases such as how ambiguous inputs are handled, whether multiple results are returned, or error behavior. This is adequate for a simple read-only tool but lacks explicit safety or limitation notes.

    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, well-structured sentence that front-loads the input condition and clearly states the output. Every word contributes to understanding the tool's purpose and behavior, with no redundancy or 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?

    Given the tool's simplicity (one parameter, no output schema), the description sufficiently conveys what the tool does, what input to provide, and what the explanation covers. It lacks details about response format or error handling, but these are not critical for a straightforward explanation tool, making it complete enough for correct invocation.

    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?

    The schema provides full coverage of the single parameter 'query' with a clear description and examples. The tool description repeats the same examples without adding new meaning beyond the schema, so it contributes no additional value, meeting the baseline for high schema coverage.

    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 action ('explain') and resource ('PostgreSQL lock mode'), and clearly defines the input type (lock mode name or operation fragment) and the output (what it blocks and which operations acquire it). This clearly differentiates from the sibling tool 'analyze_migration', which presumably focuses on migration analysis.

    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 when one needs to know about lock modes, but it does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusions or preconditions. There is no contrast with the sibling tool, so guidance is only implicit.

    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 provides substantial behavioral detail: it lists specific hazard types (table rewrites, blocking index builds, validating constraints, destructive changes) and the return format (verdict with findings and safe rewrites). It does not explicitly state whether it performs static analysis or side effects, but the linting framing implies a read-only operation.

    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 sentences long, with the first sentence packed with specific technical detail and the second giving direct usage guidance. Every word earns its place, with no redundancy or 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?

    Given the description, input schema with parameter semantics, and the presence of an output schema, the tool is well specified for an agent to select and invoke it. The only gap is not explicitly stating whether it performs static analysis or requires a database connection, but the linting context and output schema make this adequate.

    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?

    The input schema already provides full descriptions for both parameters (sql and assumeLargeTables) with 100% coverage. The tool description adds no additional parameter semantics beyond what the schema offers, so the baseline score of 3 is appropriate.

    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's function: 'Lint a PostgreSQL migration for operations that take dangerous locks' and the output verdict (PASS/REVIEW/BLOCK). It distinguishes from the sibling tool 'explain_lock' by focusing on migration linting and returning safe rewrites, not just explaining locks.

    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 explicitly says 'Call this before applying or approving any migration,' which gives clear usage context. It does not mention when not to use it or name alternative tools, but the timing guidance is direct and actionable.

    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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