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BACH-AI-Tools

City By Api Ninjas MCP Server

by BACH-AI-Tools
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'v1city' stands alone with a clear purpose, so agents cannot misselect between multiple options.

    Naming Consistency5/5

    A single tool inherently has consistent naming, as there are no other tools to compare it against. The name 'v1city' follows a pattern that includes versioning and the resource, which is straightforward and predictable in isolation.

    Tool Count2/5

    One tool is too few for a server named 'City By Api Ninjas MCP Server', which suggests a domain focused on city-related data. A single endpoint limits functionality and feels thin, as typical city APIs might include operations like search, get details, or list cities, not just one endpoint.

    Completeness2/5

    The server's purpose implies city data operations, but with only one tool ('v1city'), the surface is severely incomplete. There are obvious gaps, such as missing CRUD operations or specific queries, which will likely cause agent failures when trying to perform comprehensive city-related tasks.

  • Average 1.7/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • 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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    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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

  • Behavior1/5

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

    With no annotations provided, the description carries the full burden of disclosing behavioral traits, yet it reveals nothing about whether this is a read-only query, what data structure it returns, pagination behavior, or error conditions. The agent cannot determine if this is a safe operation or what side effects might occur.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness2/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    While the description is brief (5 words), it represents under-specification rather than efficient conciseness. The single sentence fails to earn its place by providing actionable information about the tool's functionality, wasting the opportunity to guide the agent.

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

    Completeness1/5

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

    For a tool with 9 parameters and no output schema or annotations, the description is woefully incomplete. It fails to explain the query logic (how filters combine), the return format, or the relationship to the API Ninjas service, leaving critical gaps in the agent's understanding.

    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 has 100% description coverage, documenting all 9 parameters (name, country, population ranges, coordinates, limit) adequately. The description adds no parameter-specific context, but the high schema coverage establishes a baseline score of 3 as per the evaluation guidelines.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose2/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description 'API Ninjas City API endpoint' is tautological, restating the tool's category without specifying what action it performs (e.g., search, retrieve, list). While it identifies the external service provider (API Ninjas), it lacks a specific verb or clear statement of what the tool does with city data.

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

    Usage Guidelines1/5

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

    The description provides no guidance on when to use this tool versus alternatives, nor does it mention prerequisites such as API keys, rate limits, or required parameter combinations. With no sibling tools to differentiate from, this represents a complete absence of usage guidance.

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

GitHub Badge

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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