contextfetch-mcp

ContextFetch web search

contextfetch_search

Read-only

Search the web and return LLM-ready Markdown pruned to query-relevant passages, saving tokens; use for current events, documentation, APIs, research, and fact checking.

Instructions

Search the web and get LLM-ready Markdown. Each result has its title, URL and only the passages relevant to the query (cross-encoder pruned context), so it costs far fewer tokens than raw pages. Use for current events, documentation, APIs, research and fact checking.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "contextfetch_search"
  }
}

Input Schema

Name	Required	Description
`query`	Yes	Natural-language search query.
`num_results`	No	Number of results to return (1-25, default 5).
`exclude_domains`	No	Never return results from these domains.
`include_domains`	No	Only return results from these domains, e.g. ["docs.python.org"].

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.1Oct 19, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description meaningfully adds that results are cross-encoder pruned and therefore token-cheaper than raw pages, which tells the agent what comes back. It does not mention rate limits, pagination, or freshness, so it is good but not exhaustive.

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?

Two tight sentences with zero filler; the core value proposition is front-loaded before the use-case list. Nothing is wasted.

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 no output schema, the description usefully compensates by describing the return shape (title, URL, relevant passages). Combined with full schema coverage and annotations, an agent has enough to call it, though domain-filter interaction and result-count behavior are left to the schema.

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 including the domain include/exclude lists and the 1-25 range are fully documented in the schema. The description adds no parameter syntax or format detail beyond that, 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?

States a specific verb+resource (search the web) plus its distinctive output (LLM-ready Markdown with title, URL, pruned passages). It does not name any sibling (e.g. contextfetch_research or contextfetch_answer) to draw a boundary, so it is clear but not sibling-differentiated.

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

Usage Guidelines4/5

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

"Use for current events, documentation, APIs, research and fact checking" gives concrete when-to-use contexts, which is more than implied usage. It stops short of stating when NOT to use it or which sibling supersedes it (e.g. deep_research for multi-step work), so no exclusions or alternatives are offered.

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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