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netintel_exa_search

Run neural web searches to get ranked live results with titles, URLs, dates, authors, and optional snippets. Filter by category or domain for AI agents.

Instructions

Exa neural web search for AI agents — POST or GET a query, get ranked live results (title, url, published_date, author, score) with optional highlight snippets. Filter by category (news, research paper, github, company, pdf…), domain…

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNo
queryYes
categoryNo
snippetsNo
num_resultsNo
include_domainsNo
end_published_dateNo
start_published_dateNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.61

TDQS

C2.8/5.0
Behavior3/5

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

With no annotations, the description carries the full burden and does add real value: it discloses the ranked result shape (title, url, published_date, author, score), optional highlight snippets, and that results are 'live'. It omits auth requirements, rate limits, pagination, and cost, so it is only partly transparent for a search call.

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

Conciseness3/5

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

The core purpose is front-loaded in a single efficient sentence, but the trailing 'domain…' ellipsis signals a truncated or unfinished description, which undercuts clarity and looks like copy that was cut off mid-list.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an 8-parameter tool with 0% schema coverage, no annotations, and no output schema, the description should document the tunable parameters and their formats. It covers return fields well but leaves num_results, type, and the date-range filters unexplained, so an agent cannot select good parameter values from the definition alone.

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

Parameters2/5

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

Schema description coverage is 0% across 8 parameters, so the description must compensate. It explains only 'category' (with examples), domain filtering, and snippets; it says nothing about num_results, type, or start/end_published_date, leaving half the parameters undocumented in both places.

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+resource ('Exa neural web search') and enumerates the return fields, so the agent knows exactly what kind of search this is. It does not, however, distinguish itself from closely-named siblings like netintel_exa_contents, netintel_exa_answer, or netintel_web_search, leaving the agent to guess which Exa endpoint fits.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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

The description says it is 'for AI agents' and lists filters, but never states when to choose this over netintel_exa_answer, netintel_exa_contents, or netintel_web_search, nor any prerequisite or exclusion. Usage must be inferred entirely from the 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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