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AIsa Web Search & Research

Search Mixed Content

post_scholar_search_mixed
Destructive

Search the web and academic sources together, for questions that straddle both. ⚠️ Parameters go in the query string: query (required), max_num_results, as_ylo/as_yhi. Returns a search id and results[]; the entry shape varies by source — every result has title, link and snippet, and academic ones additionally carry authors and number_of_citations, so treat those two as optional rather than assuming they are there. Measured at about 3 seconds. Use it when you do not know in advance which kind of source will answer. When you do, post_scholar_search_web or post_scholar_search_scholar is more predictable. Keep the id for post_scholar_search_explain.

Published reference price: $0.01 USD per successful request. Account-specific pricing and final settlement may differ.

Input Schema

NameRequiredDescriptionDefault
queryYesSearch query for scholarly materials
as_yhiNoYear of publication upper bound
as_yloNoYear of publication lower bound
max_num_resultsNoMaximum number of search results to return, up to 100

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Adds real operational context beyond the annotations: parameters belong in the query string, entry shape varies by source with authors/number_of_citations being optional, and latency is ~3 seconds. It does not, however, reconcile the surprising destructiveHint=true / readOnlyHint=false annotations with what is presented as a retrieval call, which is a genuine transparency gap.

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?

Front-loaded with the core purpose and scoping in the first sentence, then return shape, latency, and routing. The parameter-location warning and the pricing boilerplate make it denser than ideal, but every sentence carries information.

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?

An output schema exists so return values need not be fully restated, yet the description usefully summarizes the variable result entry shape. With annotations and schema present, the only missing piece is any explanation of the destructive/non-read-only flags.

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%, so the baseline is 3, but the description adds something the schema does not: that all four parameters are passed in the query string, with query required and as_ylo/as_yhi as bounds. It also flags that result fields authors and number_of_citations should be treated as optional.

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 combined resource ('Search the web and academic sources together') plus the scenario it serves ('questions that straddle both'). It explicitly names the two siblings it is not, so an agent can differentiate it without opening any schema.

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?

Gives an explicit positive condition ('when you do not know in advance which kind of source will answer') and the negative routing ('post_scholar_search_web or post_scholar_search_scholar is more predictable'), plus a follow-up rule to keep the id for post_scholar_search_explain. Nothing 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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