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DeepSeek × Exa Search Combine MCP

by zztdandan

Combined page fetch

fetch

Retrieves full page content from one or more URLs, trying Exa then DeepSeek in fallback order and pinning a provider when needed.

Instructions

Fetch full page content for one or more URLs. Providers are tried in the configured fallback order (Exa first by default, then DeepSeek) and the first one that returns real content wins per URL. Pass provider="exa" or "deepseek" to pin a single backend.

Input Schema

NameRequiredDescriptionDefault
urlNoA single URL to fetch.
urlsNoSeveral URLs at once. Exa handles the batch in one request.
providerNoOmit or "both" for the configured fallback order; "exa" / "deepseek" to pin one backend.
max_charsNoPer-page output cap. Default 6000.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.6/5.0
Behavior4/5

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

With no annotations at all, the description carries the full burden and delivers the key non-obvious behavior: providers are tried in a configured fallback order (Exa then DeepSeek) and the first to return real content wins per URL. That is exactly the kind of execution semantics an agent cannot infer. It omits what happens when every provider fails and whether partial batch failures are surfaced.

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?

Three tight sentences with the core purpose front-loaded and no filler. The third sentence on provider pinning partially duplicates the schema's enum description, a minor redundancy.

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

Completeness3/5

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

For a 4-parameter tool with no annotations and no output schema, the description covers execution order but not the return shape (raw text? markdown? metadata?) or failure behavior. The absence of an output schema means the description should say at least roughly what comes back, and it does not.

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 all four parameters are already documented, and the description's provider sentence largely restates the enum semantics. It adds the useful 'first one that returns real content wins per URL' framing but does not clarify the url-vs-urls relationship or when to prefer one over the other, so baseline 3 applies.

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 first sentence states a specific verb and resource — 'Fetch full page content' for 'one or more URLs' — which an agent can distinguish from 'search' (discovery) and 'status' (health). It stops short of naming the siblings explicitly, so it lands at clear-but-not-differentiated.

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?

The description explains provider selection well (fallback order, pinning with provider='exa'/'deepseek'), which is genuine usage guidance. However it never says when to reach for this tool over 'search' — e.g. after search returns URLs, or when full text rather than snippets is needed — so the when-to-use decision against alternatives is left to inference.

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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