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Project-Harrison

searoute_mcp

by Project-Harrison
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

    Each tool serves a distinct purpose: computing oceangoing route distance, geodesic great-circle distance, and full route with waypoints. No overlap or ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (compute_distance, compute_geodesic, compute_route), making them predictable.

    Tool Count5/5

    Three tools is an appropriate size for a focused maritime routing server, covering core functionality without bloat.

    Completeness4/5

    The set covers distance (route and geodesic) and route waypoints, but might lack options for routing preferences or alternative paths.

  • Average 3.4/5 across 3 of 3 tools scored. Lowest: 2.7/5.

    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
    • 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.

  • 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.

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

    Then . Browse examples.

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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

  • Behavior2/5

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

    With no annotations, the description carries full burden but only reveals that it computes oceangoing distances. It does not disclose how routes are determined (e.g., waypoints, land avoidance), processing details, or any limitations.

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

    Conciseness3/5

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

    The description is a single sentence, concise and direct. However, its brevity sacrifices important details, making it minimally acceptable.

    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 four required parameters with no schema descriptions, no annotations, and an output schema that is not described, the description is incomplete. It does not explain the return format or any additional context needed for correct invocation.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters1/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    All four parameters (start_lat, start_lon, end_lat, end_lon) have no descriptions in the schema (0% coverage). The description adds no additional meaning or constraints, leaving the agent to infer their purpose from the tool name.

    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 action (compute), the resource (shortest oceangoing route distance), and the unit (nautical miles). It effectively distinguishes from sibling tools like compute_geodesic by specifying 'oceangoing'.

    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?

    No guidance is provided on when to use this tool versus siblings. It does not mention that this is for ocean routes and should be used instead of compute_geodesic for maritime distances.

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

  • Behavior3/5

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

    Without annotations, the description carries the full burden. It reveals the route is 'oceangoing' and outputs distance and waypoints, but lacks details about algorithm, units, constraints, or side effects. Minimal but not misleading.

    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?

    A single sentence with no wasted words. Front-loads the key action and outputs. Efficient for the information provided.

    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 4 required params, no output schema, and no annotations, the description is too brief. It omits critical details like units, coordinate system, return structure, and algorithm specifics, leaving the agent underinformed.

    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?

    Schema coverage is 0%, meaning parameter descriptions are absent. The description does not explain coordinate system, format, or range. The parameter names are self-explanatory, but the description adds no value beyond the schema's titles.

    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 verb 'Compute' and the resource 'shortest oceangoing route', with qualifiers 'with distance and waypoints'. It distinguishes from sibling tools like compute_distance (likely distance-only) and compute_geodesic (likely geodesic line).

    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?

    No guidance on when to use this tool versus alternatives. The description does not mention use cases, prerequisites, or exclusions, leaving the agent without context for selection.

    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?

    Discloses key behavior: computes great-circle distance, ignores route, uses sphere model, output in nautical miles. No annotations exist, so description carries full burden; it provides the essential behavioral context. Does not mention coordinate validity or precision, but output schema likely covers return format.

    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?

    Single sentence that is tightly written with no redundant words. It efficiently conveys purpose, unit, and a key differentiator.

    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 and the presence of an output schema, the description is largely complete. It covers the core functionality and distinguishes from siblings. Minor omission: no mention of coordinate system or valid ranges, but these are common defaults.

    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?

    With 0% schema description coverage, the description adds minimal meaning beyond parameter names. It implies start and end points but does not specify coordinate format (e.g., decimal degrees) or units. The parameter names are self-explanatory, but additional detail would improve semantic clarity.

    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?

    Clearly states it computes geodesic great-circle distance in nautical miles, and explicitly distinguishes from route-based calculations. The verb 'compute' and resource 'geodesic' are specific, and the note 'ignores route' differentiates it from sibling compute_route.

    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?

    Implicitly guides usage by stating 'ignores route', indicating it is for shortest distance on a sphere, not for route planning. However, it could explicitly contrast with compute_distance or compute_route; the current description is clear enough for an informed agent.

    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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