F
licenseA
qualityC
maintenanceAn MCP server that brings Parallel web search and URL extraction to Codex and other Model Context Protocol clients.
2
22
exa
by ai.exa
Server Details
Fast, intelligent web search and web crawling.
New mcp tool: Exa-code is a context tool for coding
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- exa-labs/exa-mcp-server
- GitHub Stars
- 4,851
- Server Listing
- Exa MCP Server
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Tool Definition Quality
A4.4/5.0
Tool DescriptionsA
Average 4.5/5 across 2 of 2 tools scored.
Server CoherenceA
Disambiguation5/5
The two tools have clearly distinct purposes: web_search_exa is for discovering content via search queries, while web_fetch_exa is for extracting full content from known URLs. Their descriptions explicitly differentiate them and even mention when to use each (e.g., use web_fetch_exa after web_search_exa if highlights are insufficient). There is no ambiguity or overlap in functionality.
Naming Consistency5/5
Both tools follow a consistent snake_case naming pattern with the prefix 'web_' and descriptive suffixes ('search_exa' and 'fetch_exa'). This makes them easily recognizable as part of the same set and clearly indicates their roles (searching vs. fetching) within the web domain.
Tool Count3/5
With only 2 tools, the set feels thin for a web-related server, as it lacks operations like updating, deleting, or managing search history. However, the tools cover core search and fetch workflows effectively, making it borderline but functional for basic web content retrieval.
Completeness3/5
For a web search and content extraction domain, the tools provide search and fetch capabilities, which are essential. However, there are notable gaps, such as no tools for filtering results, saving searches, or handling errors (e.g., retrying failed fetches). The surface is minimal but covers the primary use cases without dead ends.
Available Tools
2 toolsweb_fetch_exaARead-onlyIdempotentInspect
Read a webpage's full content as clean markdown. Use after web_search_exa when highlights are insufficient or to read any URL.
Best for: Extracting full content from known URLs. Batch multiple URLs in one call. Returns: Clean text content and metadata from the page(s).
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| urls | Yes | URLs to read. Batch multiple URLs in one call. | |
| maxCharacters | No | Maximum characters to extract per page (default: 3000) |
Tool Definition Quality
A4.2/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds useful context about returning clean markdown and metadata, and supporting batching. However, it claims 'full content' while the schema includes a maxCharacters default of 3000, and the description does not disclose that pages are truncated unless the limit is increased, which is a significant gap in behavioral transparency.
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 three short sentences, each adding value: the core function, usage guidance, and return expectation. It is front-loaded with the most important information and avoids any fluff or repetition.
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 read tool with two parameters and strong annotations, the description is mostly complete: it states the input (URLs), output (text and metadata), and when to use it. The only notable omission is the character limit caveat, which prevents a perfect completeness score.
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?
The schema already provides descriptions for both parameters (urls and maxCharacters), covering 100% of the parameters. The description contributes little beyond repeating the batching capability already in the schema, so it does not add meaningful extra meaning beyond the structured data.
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 reads a webpage's full content as clean markdown, with an explicit verb and resource. It also distinguishes from the sibling tool web_search_exa by specifying its use case: after search when highlights are insufficient or for reading any URL.
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 explicitly says to use this tool after web_search_exa when highlights are insufficient, and it highlights best uses (extracting full content from known URLs, batching multiple URLs). This provides clear when-to-use guidance and names the alternative, leaving no ambiguity about when to choose this tool over the sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
web_search_exaARead-onlyIdempotentInspect
Search the web for any topic and get clean, ready-to-use content.
Best for: Finding current information, news, facts, people, companies, or answering questions about any topic.
Returns: Clean text content from top search results.
Query tips:
describe the ideal page, not keywords. "blog post comparing React and Vue performance" not "React vs Vue".
Use category:people / category:company to search through Linkedin profiles / companies respectively.
If highlights are insufficient, follow up with web_fetch_exa on the best URLs.ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Natural language search query. Should be a semantically rich description of the ideal page, not just keywords. Optionally include category:<type> (company, people) to focus results — e.g. 'category:people John Doe software engineer'. | |
| numResults | No | Number of search results to return (default: 10). |
Tool Definition Quality
A4.7/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already communicate readOnly, openWorld, idempotent, and non-destructive behavior. The description adds useful behavioral context: it returns clean text content from top results, supports category:people and category:company filters, and suggests a follow-up workflow with web_fetch_exa. This goes beyond the basic annotation hints.
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 well-structured with clear sections ('Best for', 'Returns', 'Query tips') and front-loaded with the core purpose. Every sentence contributes practical information, and there is no redundancy or filler.
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?
Despite lacking an output schema, the description explains what the tool returns ('clean text content from top search results'). With two well-documented parameters, clear use context, and explicit sibling relationship, the description is fully complete for an agent to select and invoke the tool correctly.
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?
Schema coverage is 100% for both parameters, so the baseline is 3. The description enriches the query parameter semantics by advising users to describe the ideal page rather than keywords, and by giving a concrete illustrative example. This adds meaningful guidance 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 identifies the tool as a web search that returns clean, ready-to-use content. It distinguishes itself from the sibling web_fetch_exa by framing search as the initial step and fetch as the follow-up for specific URLs.
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 states explicitly when this tool is best ('current information, news, facts, people, companies') and provides practical query tips. It also names the alternative web_fetch_exa and gives a clear condition for when to use it ('If highlights are insufficient').
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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