Skip to main content
Glama
hec-ovi
by hec-ovi

web_search

Query multiple web search engines and receive fused, deduplicated results for research or fact-checking.

Instructions

Search the web across multiple engines and return ranked, deduplicated results.

Use this when the user wants to look something up online, find current information, research a topic, or check a claim against live sources. Results are fused across engines (provenance-aware rank fusion) and deduplicated. Each result carries a human-readable handle; after you web_fetch its URL you can web_open that handle to page through the document.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe search query, e.g. "rust ownership model" or "site:nature.com crispr".
max_resultsNoHow many results to return (default 8). Raise for research, lower for a quick lookup.
detailNo"concise" (default) omits per-result engines and score to save tokens; "detailed" includes them.concise
enginesNoEngine names to query (e.g. ["searxng", "ddgs"]); omit for all configured.
countryNoISO 3166-1 alpha-2 country code (e.g. "us"); omit for engine default.
languageNoISO 639-1 language code (e.g. "en"); omit for engine default.
freshnessNoOne of "any", "day", "week", "month", "year" (best-effort recency).any
safesearchNoOne of "off", "moderate", "strict".moderate
siteNoRestrict to a single host (e.g. "docs.python.org").
offsetNoAdvanced result offset. Best-effort only: the keyless backends do not page reliably, so to get different results prefer refining the query.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations provided, so description carries the burden. It describes fusion (provenance-aware rank fusion), dedup, and the handle workflow. It doesn't explicitly state it's read-only, but search is inherently idempotent and non-destructive.

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?

The description is ~80 words, front-loaded with core purpose, followed by usage guidance and behavioral details. Every sentence serves a purpose with no fluff.

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?

Given an output schema exists, return values need not be explained. The description covers purpose, usage, behavior, and workflow. It could add minor edge cases (e.g., no results), but overall it's sufficient for a search tool.

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 100%, so baseline is 3. The description adds overall behavioral context but doesn't add parameter-level meaning beyond the schema descriptions. It mentions the handle workflow but that's output-oriented.

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?

The description clearly states the verb (search), resource (web), and key differentiators (multiple engines, ranked, deduplicated). It distinguishes from siblings web_fetch and web_open which are for fetching/opening specific URLs.

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 explicitly says when to use (look up info, research, check claims) and mentions the handle workflow. It doesn't have explicit exclusions, but the sibling tools cover different use cases, making context clear.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/hec-ovi/websearch-skill'

If you have feedback or need assistance with the MCP directory API, please join our Discord server