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Lulu-The-Narwhal

postal-lookup-mcp

by Lulu-The-Narwhal
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusing it with others. The description clearly delineates what it does and does not do (e.g., not reverse lookup or full address lookup), so an agent can unambiguously select it for postal-code-to-place queries.

    Naming Consistency5/5

    The single tool name 'lookup_postal_code' follows a clear verb_noun pattern and is self-descriptive. Consistency is trivially satisfied since there's only one name, but the name itself is well-structured and aligns with common conventions.

    Tool Count4/5

    One tool is below the typical 3-15 range, but the server's scope is extremely narrow: resolving postal codes to place information. The single tool fully addresses this purpose, so the count feels slightly under but reasonable rather than thin or inadequate.

    Completeness5/5

    The tool provides complete coverage for its stated domain: it resolves postal codes to place names, region, and coordinates, handles multi-place spans, and returns error dicts for unrecognized codes. No obvious missing operations for the declared purpose of postal code lookup.

  • Average 5/5 across 1 of 1 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.

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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?

    The readOnlyHint annotation is present, and the description adds valuable behavioral context beyond it: description of multi-place postal codes, retourning all matches, and the error dict behavior (never raises). These are non-obvious behaviors that help the agent anticipate outcomes.

    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 well-structured and front-loaded with purpose, followed by usage exclusions and behavioral edge cases. Every sentence adds distinct value, with no redundancy or filler.

    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 the tool's moderate complexity (2 params, multiple return possibilities), the description covers purpose, parameter formats, usage exclusions, edge cases, and error behavior. It provides a complete picture even without an output schema.

    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 description coverage is 0%, but the description fully compensates by explaining both parameters: country_code as a 2-letter ISO code with examples, and postal_code with country-specific format examples. This provides essential semantic detail missing from the 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 ('Resolve') and clearly identifies the resource (postal/ZIP code to place names, state/region, coordinates). It distinguishes itself from reverse lookups and full street address lookup, 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?

    The description provides explicit when-to-use examples ('what city is ZIP 90210 in') and states what it is not for ('not for the reverse... or for full street address lookup'). This clearly guides an agent on when to select this tool over 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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  • Confirm that there are no obvious security issues.
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