Skip to main content
Glama
BACH-AI-Tools

Geocoding By API Ninjas

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

    The two tools have perfectly distinct purposes: one converts addresses to coordinates (geocoding) and the other converts coordinates to addresses (reverse geocoding). There is no overlap or ambiguity between them, making it impossible for an agent to confuse their functions.

    Naming Consistency5/5

    Both tools follow a consistent naming pattern with 'v1' prefix and descriptive suffixes ('geocoding' and 'reversegeocoding'). They use the same style (lowercase with no separators) and clearly indicate their functions, ensuring predictability and readability.

    Tool Count2/5

    With only 2 tools, the server feels thin for a geocoding service, as it lacks operations like batch processing, validation, or additional geospatial queries. While the core functions are covered, the limited scope may hinder more complex agent workflows that expect broader capabilities.

    Completeness3/5

    The server covers the basic bidirectional conversion between addresses and coordinates, but there are notable gaps such as missing batch operations, address validation, or support for different coordinate formats. This limits the surface for handling varied geocoding tasks, though agents can still perform fundamental lookups.

  • Average 2.2/5 across 2 of 2 tools scored. Lowest: 1.7/5.

    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.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

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

    Then . Browse examples.

  • Add related servers to improve discoverability.

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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. The description does not reveal any behavioral traits such as rate limits, authentication needs, error handling, or what the tool returns (e.g., coordinates). It lacks essential details for a tool that likely performs external API calls.

    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?

    The description is a single phrase 'API Ninjas Geocoding API endpoint', which is overly concise to the point of under-specification. It fails to front-load key information about the tool's function, making it inefficient for quick understanding. While brief, it lacks substance and does not earn its place as a helpful description.

    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?

    Given the complexity of a geocoding tool (likely involving external API calls and coordinate outputs), the description is incomplete. No annotations exist, and there is no output schema, so the description should compensate by explaining return values or behavior, but it does not. It fails to provide enough context for effective use.

    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, with clear parameter descriptions (e.g., 'City name', 'US state (for United States cities only)'). The description adds no additional meaning beyond the schema, but the schema is comprehensive, so the baseline score of 3 is appropriate as it adequately documents parameters without extra value from the description.

    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 Geocoding API endpoint' restates the tool name 'v1geocoding' and adds minimal context (the provider name). It does not specify what the tool does (e.g., convert addresses to coordinates), making the purpose vague. It distinguishes from the sibling 'v1reversegeocoding' only by name, not by functional difference.

    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?

    No guidance is provided on when to use this tool versus alternatives. The description does not mention the sibling tool 'v1reversegeocoding' or any other context for usage. There is no indication of prerequisites, constraints, or typical scenarios for application.

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

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure but offers minimal information. It doesn't describe whether this is a read-only operation, what kind of data it returns (e.g., address components), potential rate limits, authentication requirements, or error conditions. The description is essentially a label rather than behavioral guidance.

    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 extremely concise - a single phrase that identifies the API service. While it's under-specified in terms of content, it's not verbose or poorly structured. Every word earns its place by identifying the provider and endpoint type.

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

    Completeness2/5

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

    Given the tool's purpose (geospatial conversion) and the absence of both annotations and an output schema, the description is insufficient. It doesn't explain what the tool returns (address information), doesn't provide context about the API provider's limitations or requirements, and doesn't help an agent understand how to interpret results. For a tool with no structured behavioral hints, the description should do more heavy lifting.

    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, with clear documentation for both 'lat' (latitude coordinate) and 'lon' (longitude coordinate). The description adds no parameter information beyond what's in the schema. According to scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description.

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

    Purpose3/5

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

    The description 'API Ninjas Reverse Geocoding API endpoint' states the tool's general purpose (reverse geocoding) but is vague about what it actually does. It doesn't specify the verb (e.g., 'convert', 'look up', 'retrieve') or the resource (e.g., 'address', 'location details'), and doesn't distinguish it from its sibling 'v1geocoding' which presumably does forward geocoding. The description is functional but lacks specificity.

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

    Usage Guidelines2/5

    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. It doesn't mention the sibling tool 'v1geocoding' or explain that reverse geocoding converts coordinates to addresses while forward geocoding does the opposite. There's no context about prerequisites, limitations, or typical use cases.

    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.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

bachai-geocoding-by-api-ninjas MCP server

Copy to your README.md:

Score Badge

bachai-geocoding-by-api-ninjas MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/BACH-AI-Tools/bachai-geocoding-by-api-ninjas'

If you have feedback or need assistance with the MCP directory API, please join our Discord server