Skip to main content
Glama
x15907982411

exa-mcp-server-for-operit

by x15907982411
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v3.4.0-operit.1

  • Disambiguation5/5

    web_search_exa and web_fetch_exa have clearly distinct purposes: one searches the web and returns highlights, the other fetches full page content. There is no overlap in functionality, and their complementary nature is explicitly described.

    Naming Consistency5/5

    Both tools follow the same verb_noun pattern with a consistent shared prefix: web_search_exa and web_fetch_exa. The naming is predictable and reflects the action each tool performs.

    Tool Count4/5

    Two tools is minimal but appropriate for a server focused on web search and retrieval. The pair forms a complete workflow (search then fetch), and adding more tools for a simple utility like this could be overkill. The count feels slightly thin but not insufficient for the stated purpose.

    Completeness4/5

    The server covers the core lifecycle of web content retrieval: searching for relevant pages and fetching full content from known URLs. Minor gaps exist, such as no findSimilar or advanced filtering, but the essential use case is fully supported.

  • Average 4.5/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 12 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.

  • 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

  • Behavior4/5

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

    Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so safety is covered. The description adds behavioral context beyond annotations, such as the ability to batch multiple URLs in one call and the return of clean text plus metadata.

    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 compact and front-loaded with the core purpose. It uses a clear structure with 'Best for' and 'Returns' sections, and each sentence serves a distinct purpose without unnecessary detail.

    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?

    For a relatively simple read-only tool, the description covers the essential context: what it does, when to use it, batching capability, and return value type. The annotations handle safety, and the schema covers parameters, leaving no significant gaps for an agent to select and invoke the tool.

    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 100%, so both parameters (urls and maxCharacters) are fully documented in the schema. The description reinforces batching behavior for the urls parameter but adds no new semantic details 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 'Read a webpage's full content as clean markdown', which is a specific verb+resource combination. It also distinguishes itself from sibling web_search_exa by focusing on fetching full content from URLs 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 Guidelines5/5

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

    Explicitly says 'Use after web_search_exa when highlights are insufficient or to read any URL', providing a direct usage context and alternative. The 'Best for' note further clarifies when to use the tool.

    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 readOnlyHint=true, idempotentHint=true, destructiveHint=false, so safety is covered. The description adds behavioral/return context: 'Returns: Clean text content from top search results' and query tips describing ideal-page phrasing. No contradiction with annotations.

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

    Conciseness4/5

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

    Description is multi-paragraph but well-structured with labeled sections (Best for, Returns, Query tips) and front-loaded with the core purpose. It's slightly longer than minimal, but each section provides useful operational guidance without fluff.

    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?

    For a simple 2-parameter search tool with no output schema, the description covers the use case, return format, query strategy, and the relationship to web_fetch_exa. It doesn't specify pagination or error behavior, but that's not required given the tool's simplicity and annotations.

    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?

    Input schema covers both parameters with descriptions (100% coverage), so baseline is 3. The description adds extra semantics for the query parameter: examples of natural language phrasing and category:people/category:company syntax, which goes beyond the schema's property description.

    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 opens with 'Search the web for any topic and get clean, ready-to-use content' – a specific verb+resource+outcome. It also distinguishes from the sibling web_fetch_exa by explicitly mentioning follow-up with that tool on URLs, clarifying that this tool searches rather than fetches.

    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?

    Explicitly names 'Best for' use cases (current information, news, facts, people, companies) and gives direct alternative guidance: 'If highlights are insufficient, follow up with web_fetch_exa on the best URLs.' This clearly tells when to use this tool versus the sibling.

    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

exa-mcp-server-for-operit MCP server

Copy to your README.md:

Score Badge

exa-mcp-server-for-operit 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/x15907982411/exa-mcp-server-for-operit'

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