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gabrimatic

MCP Web Search Tool

by gabrimatic
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v2.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: web_search for searching and fetching a list of results, and fetch_url for retrieving the full content of a specific result. There is no overlap in functionality.

    Naming Consistency5/5

    Both tool names follow a consistent snake_case verb_noun pattern ('web_search' and 'fetch_url'), making them predictable and easy to understand.

    Tool Count4/5

    With only two tools, the server is minimal but well-scoped for its purpose of web search and content retrieval. While a few more tools could enhance completeness, the current count is appropriate for a focused utility.

    Completeness4/5

    The server covers the essential workflow of search then fetch, with pagination support via cursors. Missing advanced search features like filtering or sorting, but these are not critical for basic use.

  • Average 4.6/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 2 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 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.

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  • 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

  • Behavior4/5

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

    No annotations exist, so the description carries the burden. It discloses that results are ranked summaries with stable ids and that text is untrusted. However, it does not explicitly state read-only or other potential side effects, but for a search tool this is reasonable.

    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?

    Three sentences, all essential. Front-loaded with usage guidance and workflow, no wasted 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 11 parameters with high schema coverage, no output schema, and a sibling tool, the description explains the workflow, result nature, and caution. It is complete for the tool's purpose.

    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 description coverage is 91%, so the schema already explains almost all parameters. The description adds no additional parameter-level meaning beyond implying search_term is the query. Baseline score of 3 is appropriate.

    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 this tool is for web search of current, source-backed answers like news, prices, and weather. It distinguishes itself from the sibling fetch_url by specifying a two-step workflow: use this to get stable ids, then fetch_url for details.

    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?

    It explicitly says 'Use this first' and instructs to call fetch_url before trusting exact details. It also warns that returned text is untrusted, providing clear usage context.

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

  • Behavior5/5

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

    Given no annotations, the description fully details behavior: returns page title, readable text, outbound links, next_cursor on truncation, refusal of certain URLs, and warns that content is untrusted. This is comprehensive for a read-only tool.

    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?

    Three purposeful sentences with no waste. The first sentence states usage and purpose immediately. Each subsequent sentence adds essential behavioral info. Structure is efficient and front-loaded.

    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?

    Despite no output schema, the description covers key return fields (title, text, links, cursor) and constraints. Minor gap: no explicit mention of error behavior for invalid URLs/IDs, but overall it is sufficient for a tool with four parameters and good annotations.

    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 coverage is 100%, and the description adds value: explains preferred parameter (id_or_url), clarifies cursor and max_chars semantics (soft cap), and notes that URL is deprecated. This goes beyond the schema descriptions.

    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's purpose: to read content of a search result after a search. It specifies the verb 'read' and the resource 'actual content of a result', and distinguishes from sibling web_search by indicating it should be used after a search.

    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?

    Provides explicit context: 'Use this after a search' and 'Pass either a search result id (preferred) or a full http(s) URL.' It implies when to use (after search) and excludes non-http(s) and private hosts. However, it does not explicitly mention alternatives beyond the sibling tool name.

    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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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