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jackedelic

exa-search

by jackedelic
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one is specialized for programming-related searches (code context), while the other is for general web searches. There is no overlap in functionality, and the descriptions explicitly differentiate their use cases.

    Naming Consistency5/5

    Both tools follow a consistent naming pattern: they use snake_case and start with a verb (get, search) followed by a noun phrase (code_context, web). This makes them predictable and easy to understand.

    Tool Count2/5

    With only two tools, the server feels under-scoped for a search domain. While the tools cover distinct areas (code and web), a search server typically benefits from more granular operations like filtering, pagination, or specialized search types, making this set feel thin.

    Completeness3/5

    The tools cover two key search domains (code and web), but there are notable gaps. For example, there are no tools for advanced search configurations, result management, or handling different content types beyond code and general web pages, which limits flexibility for agents.

  • Average 4.1/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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.

  • This repository includes a glama.json configuration file.

  • 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.

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How to sync the server with GitHub?

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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=true, idempotentHint=true, and destructiveHint=false, so the agent knows this is a safe, repeatable read operation. The description adds value by mentioning 'highest quality and freshest context' and the programming domain focus, but doesn't disclose additional behavioral traits like rate limits, authentication needs, or response format details beyond what annotations provide.

    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?

    The description is appropriately sized with three sentences that each serve a purpose: stating the tool's purpose, highlighting quality/freshness, and providing usage rules. It's front-loaded with the core function, though the mandatory rule could be more concise.

    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 moderate complexity (2 parameters, no output schema), the description is reasonably complete. It covers purpose, domain, quality claims, and usage rules. With annotations handling safety profiles and schema covering parameters, the main gap is lack of output format explanation, but this is acceptable since no output schema exists.

    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 the schema fully documents both parameters (query and tokensNum) with descriptions and examples. The description doesn't add any parameter-specific semantics beyond what's in the schema, maintaining the baseline score of 3 since the schema carries the full burden.

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

    Purpose4/5

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

    The description clearly states the tool's purpose: 'Search and get relevant context for any programming task' with specific resources mentioned ('libraries, SDKs, and APIs'). It distinguishes from the sibling tool 'web_search_exa' by specifying programming-related queries, though it doesn't explicitly contrast their differences beyond domain focus.

    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?

    The description provides explicit usage guidelines: 'Use this tool for ANY question or task related to programming' and includes a mandatory rule: 'when the user's query contains exa-code or anything related to code, you MUST use this tool.' This gives clear when-to-use instructions, though it doesn't specify when NOT to use it or alternatives beyond the sibling 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, and destructiveHint=false, covering safety and idempotency. The description adds valuable behavioral context beyond annotations: it specifies real-time capability, scraping functionality, configurable result counts, and that it returns content from relevant websites. No contradiction with annotations exists.

    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 front-loaded with the core purpose in the first sentence, followed by supporting details. Every sentence adds value: the first establishes the main function, the second explains scraping capability, and the third covers configurability and return behavior. No wasted words or redundancy.

    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 search tool with rich annotations (covering safety and idempotency) and 100% schema coverage, the description provides good contextual completeness. It explains the tool's real-time nature, scraping capability, and return behavior. The main gap is the absence of an output schema, but the description partially compensates by mentioning what gets returned ('content from the most relevant websites').

    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%, providing detailed documentation for all 5 parameters. The description mentions 'configurable result counts' which aligns with the 'numResults' parameter, but doesn't add significant meaning beyond what the schema already specifies. Baseline 3 is appropriate given the comprehensive schema coverage.

    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 with specific verbs ('search the web', 'scrape content') and resources ('Exa AI', 'web', 'specific URLs'). It distinguishes from the sibling tool 'get_code_context_exa' by focusing on general web search rather than code-specific context retrieval.

    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 provides clear context for when to use this tool ('performs real-time web searches', 'can scrape content from specific URLs'), but doesn't explicitly state when not to use it or mention alternatives beyond the implied distinction from the sibling tool. It lacks explicit exclusions or comparison to other search methods.

    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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