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Glama

browser_search

Search several engines at once and interleave results by engine and domain to prevent one ranking from deciding what you read; captcha-blocked engines are reported and skipped.

Instructions

Research search across several engines at once (default naver, google, bing, daum, duckduckgo) so one engine's ranking does not decide what you read. Results are interleaved one engine at a time, one per domain; each says which engines listed it and how often you already used that domain (familiar). Read from several domains and engines, prefer unfamiliar ones, and do not settle on the first source. Engines that show a captcha are reported as blocked and skipped.

Input Schema

NameRequiredDescriptionDefault
portNoChrome debug port. Give it to browser_launch/browser_open once; tab tools find the port from the tab. Never 9222/9223 unless the human said so: those are people's logged-in profiles.
queryYes
enginesNo
per_domainNopicks per domain (default 1)
per_engineNoresults kept per engine (default 8)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses interleaving order (one engine at a time), per-domain deduplication, per-result provenance fields and a familiarity count, plus captcha-blocked engines being reported and skipped. It stops short of covering latency, total result limits, or partial-failure reporting beyond captcha.

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?

Four sentences, purpose front-loaded first, then output shape, then reading guidance, then failure behavior. Dense but every sentence contributes; the reading-guidance sentence is the only one that could be trimmed.

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 and no annotations, the description does the heavy lifting by explaining what each result carries and how results are ordered. It is nearly complete for a search tool; only error/timeout behavior and result-count limits are unaddressed.

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 coverage is 60%: port, per_domain and per_engine already have useful descriptions, and the engines enum is self-documenting. The description adds real value by stating the default engine set, but says nothing about query semantics or how per_engine/per_domain interact with the interleaving it describes, so the baseline 3 fits.

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 (multi-engine research search) and names the concrete engines involved. It is clearly distinguishable from the tab-control siblings (browser_goto, browser_open, browser_read) because it produces ranked, cross-engine result sets rather than navigating to a page.

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?

It gives downstream reading guidance ('read from several domains and engines, prefer unfamiliar ones, do not settle on the first source'), which implies a research use case. However, it never states when to choose this over the sibling browser tools or when not to use it, leaving tool selection to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.