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mcp-exa

by algonacci
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

exa_search

Search the web with Exa to return ranked results with page content. Filter by domains, dates, categories, and content type (highlights or full text).

Instructions

Search the web with Exa and return ranked results with page content.

Args:
    query: Natural-language search query (e.g., "AI consultant companies in Indonesia")
    num_results: Number of results (1-25, default: 10; each result above 10 costs extra)
    type: "auto" (default), "fast", "instant" (cheapest) or "neural"
    category: Optional focus: "company", "news", "publication", "personal site",
        "financial report" or "people"
    include_domains: Only return results from these domains (e.g., ["arxiv.org"])
    exclude_domains: Never return results from these domains
    start_published_date: Only results published on/after this date (YYYY-MM-DD)
    end_published_date: Only results published on/before this date (YYYY-MM-DD)
    content: "highlights" (default, relevant snippets), "text" (full page text) or "none"
    max_characters: Max characters of page text per result when content="text" (default: 3000)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoauto
queryYes
contentNohighlights
categoryNo
num_resultsNo
max_charactersNo
exclude_domainsNo
include_domainsNo
end_published_dateNo
start_published_dateNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It does add genuine behavioral context with the cost signal ('each result above 10 costs extra') and the default content mode, but says nothing about auth/API key requirements, rate limits, latency differences between search types, or pagination.

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?

Front-loads a one-line purpose, then a tight docstring-style arg list where each line earns its place. No filler, though the block is long by necessity given 10 undocumented parameters.

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?

An output schema exists, so return values need not be explained, and all input parameters plus cost implications are covered. The only real gap is sibling disambiguation and any auth/rate-limit context, which matters for a search tool with several near-neighbor variants.

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 description coverage is 0%, so the description must do all the work, and it does: every one of the 10 parameters gets meaning, valid values, defaults, ranges, or an example (query, num_results 1-25 + cost, type enum with cost ranking, category enum, include/exclude domains with examples, YYYY-MM-DD date formats, content modes, max_characters default). This is a strong compensation for an otherwise bare schema.

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?

States a specific verb and resource ('Search the web with Exa') plus the output shape ('ranked results with page content'), which is enough to distinguish it from the content-fetching sibling. However, it never contrasts itself with exa_deep_search or exa_research_start, so an agent must guess which search variant fits.

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?

Usage guidance is implied rather than stated: 'instant (cheapest)' and the cost note on num_results help pick values, but there is no explicit when-to-use-this-tool-vs-siblings statement or prerequisite/exclusion guidance. The agent is left to infer that deep/research siblings handle heavier cases.

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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