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Crawlora MCP

Crawlora MCP

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by Crawlora-org
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JavaScript
Remote

yahoo_search_suggest

Get Yahoo search autocomplete suggestions for a partial query. Returns suggested terms with optional entity metadata like type, image, or description for known entities.

Instructions

Yahoo web search autocomplete suggestions. Returns Yahoo's own search-box autocomplete suggestions for a partial query: a flat list of suggested search terms, each optionally carrying knowledge-panel entity metadata (type, image, subtitle, description) when Yahoo resolves the term to a known company, place, product, or similar entity rather than a plain phrase.

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": {
      "q": "<q>"
    },
    "name": "yahoo_search_suggest"
  }
}

Input Schema

NameRequiredDescriptionDefault
qYesPartial search query to autocomplete
countNoNumber of suggestions to return, default 10, clamped to 1..20

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.14.0

TDQS

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full transparency burden. It discloses the key behavioral variation — output entries carry entity metadata only when Yahoo resolves the term to a known entity rather than a plain phrase — and the flat-list return shape. It does not mention rate limits or network access, but for a read-only autocomplete tool this is an acceptable disclosure level.

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?

A single sentence that front-loads the tool's purpose, then precisely describes the return shape and the optional entity variant. Every clause earns its place; it reads slightly dense but contains zero filler words.

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 low-complexity tool with two fully documented parameters and no output schema, the description adequately covers the use case, the response structure, and the metadata variant. The only shortfall is that it never explicitly calls out sibling alternatives like yahoo_news_suggest, but the Yahoo-web-search scoping largely compensates for that.

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 'q' and 'count' are already fully documented, including the default of 10 and the 1..20 clamp. The description echoes the 'partial query' semantics and adds useful output context about entity metadata, but it does not add parameter-level syntax that the schema lacks. Baseline 3 is appropriate.

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?

States a specific verb and resource: it returns Yahoo's own search-box autocomplete suggestions for a partial query. It is precise about the output shape — flat list of terms with optional knowledge-panel entity metadata (type, image, subtitle, description) — which distinguishes it at a glance from the many sibling suggest tools (google_suggest, bing_suggest, zalando_suggest) and from yahoo_search, which returns actual results.

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?

The description clearly situates the tool for partial-query autocomplete use and scopes it to Yahoo's web search box, differentiating it from engine-specific siblings like google_suggest or bing_suggest. It does not explicitly enumerate when-not-to-use conditions or point to an alternative sibling, but the context is unambiguous enough for an agent to select it correctly.

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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