Skip to main content
Glama
mnm-matin

captain-search

by mnm-matin
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

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 clearly distinct purpose: web search, code search, and fetching a specific URL. There is no overlap between the tools, so an agent can easily select the correct one.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with underscores: search_web, fetch_webpage, search_code. This makes the tool set predictable and easy to navigate.

    Tool Count4/5

    Three tools is slightly minimal for a search-focused server, but it covers the core operations of web search, code search, and page fetching. The count is reasonable for a niche tool set.

    Completeness4/5

    The tool set covers the primary search and fetch workflows, but it lacks options like image or news search. Minor gaps exist, but agents can accomplish most tasks without dead ends.

  • Average 4.1/5 across 3 of 3 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 failing

  • 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

  • Behavior3/5

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

    Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds that results are in markdown format and that multiple providers are searched, but it does not disclose pagination, rate limits, or provider-specific behaviors.

    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 concise, with three short labeled sections (Overview, Args, Returns). Every sentence serves a purpose, and the key information appears early.

    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 simplicity (2 params, 1 required), the presence of a complete schema and output schema, and annotations covering safety, the description is sufficiently complete. It states purpose, parameters, and return format, which is all a caller needs for basic 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?

    Schema coverage is 100%, so a baseline of 3 is appropriate. The description's Arg section mostly repeats the schema's parameter descriptions without adding meaningful new semantics, though it does briefly restate the repo scoping concept.

    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 ('Search') with a clear resource ('code') and adds scope ('across multiple providers'). This distinguishes it from sibling tools like search_web and fetch_webpage.

    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 from the tool name and the phrase 'Search code', but it never explicitly states when to use this tool over search_web or fetch_webpage, nor does it mention exclusions or prerequisites.

    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?

    The description adds context beyond annotations by mentioning the weighted selection or multi-provider modes and stating that results are returned in markdown format. Annotations already declare readOnlyHint=true and destructiveHint=false, so the safe, read-only nature is covered. However, the meaning of 'weighted selection' is not explained, which could confuse an agent.

    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 concise and well-structured with Args and Returns sections. It avoids redundancy and provides essential information in a clear format.

    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?

    The tool is simple, has an output schema, and the description covers the key aspects. The main gap is the lack of guidance on when to choose this tool over siblings, and some ambiguity around 'weighted selection.' However, for a straightforward search tool, the description is adequately complete.

    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 complete parameter descriptions (100% coverage). The description repeats the parameters with minimal added value: it clarifies that max_results is per provider in multi mode and specifies the default provider as auto. This is helpful but does not go far beyond 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 clearly states it searches the web ('Search the web using weighted selection or multi-provider search'), which is a specific verb and resource. It distinguishes itself from sibling tools like search_code and fetch_webpage by its focus on general web search.

    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 does not explicitly specify when to use this tool vs alternatives. While the name and scope make it obvious, there is no explicit guidance such as 'for fetching a specific URL, use fetch_webpage instead.' Usage is implied rather than stated.

    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?

    Annotations already declare the tool as read-only, non-destructive, and idempotent. The description adds behavioral context about Jina Reader, JavaScript handling, PDF extraction, and fallback to Trafilatura, providing useful transparency beyond annotations.

    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 with clear sections (Args, Returns, Examples) and is appropriately sized for the tool's simplicity. No unnecessary words.

    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 simple single-parameter tool with an output schema, the description fully covers usage, return format, and fallback behavior. Examples further complete the picture.

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

    Parameters4/5

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

    Schema coverage is 100%, so the baseline is 3. The description adds meaning by clarifying that the URL can be a web page or PDF and provides examples, enhancing the semantic understanding.

    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 fetches and extracts content from web pages or PDFs, using a specific verb and resource. It distinguishes from sibling search tools by focusing on fetching a known URL rather than searching.

    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 implies usage when a URL is available, and examples demonstrate both web page and PDF fetches. It does not explicitly exclude alternatives, but the context of fetching a specific URL is clear relative to search tools.

    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

captain-search MCP server

Copy to your README.md:

Score Badge

captain-search 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/mnm-matin/captain-search'

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