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mcp-exa

by algonacci
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

exa_get_contents

Retrieve and extract readable text from known web pages via Exa, including JavaScript-heavy or blocked sites. Limit results to query-relevant passages when needed.

Instructions

Read the content of known web pages through Exa (handles JS-heavy and blocked sites).

Args:
    urls: Page URLs to read (max 10)
    highlight_query: Optional; return only the passages relevant to this query
        instead of the full text (e.g., "IEEE papers")
    max_characters: Max characters of text per page when no highlight_query (default: 5000)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYes
max_charactersNo
highlight_queryNo

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 must disclose behavioral traits. It usefully notes that the tool handles JS-heavy and blocked sites and that highlight_query changes the return from full text to relevant passages; however, it omits auth requirements, rate limits, error behavior, and explicit read-only safety, leaving the behavioral picture incomplete.

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 front-loaded with the purpose, then lists three parameters in a compact Args block. It wastes little space, though the Args formatting is slightly mechanical; overall it is appropriately sized.

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?

With an output schema present, the description need not explain return values, and it covers purpose, all parameters, and key capabilities. It leaves out auth/rate-limit context and explicit read-only signaling, but for a straightforward content-fetch tool it is largely complete.

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 carries the full parameter burden. It does so thoroughly: it states urls accepts up to 10 page URLs, explains that highlight_query returns only relevant passages instead of full text (with an example), and clarifies that max_characters applies only when no highlight_query is given, plus its 5000 default.

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 states a specific verb+resource ('Read the content of known web pages') and distinguishes the tool from search siblings by specifying 'known' pages and noting JS-heavy/blocked site handling. It does not explicitly name an alternative, but the scope is clear enough for an agent to select it.

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?

It implies usage ('known web pages') but offers no explicit when-to-use guidance, no prerequisites, and no named alternatives among exa_search, exa_deep_search, etc. An agent can infer it is for URLs already in hand, but the description stops short of actionable routing guidance.

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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