web_search
web_searchLive web search: fresh results + short AI answer. ~$0.005.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Search query |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | No |
web_searchLive web search: fresh results + short AI answer. ~$0.005.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Search query |
| Name | Required | Description | Default |
|---|---|---|---|
| result | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate openWorldHint=true and destructiveHint=false, but the description adds valuable specifics: the tool performs a live search (not cached), returns a short AI-generated answer, and has an approximate cost of $0.005. These details go beyond the annotations and help the agent anticipate behavior and cost. No contradiction with annotations is present.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, compact sentence that front-loads the core purpose and includes essential extras (freshness, AI answer, cost) without any redundant words. Every element earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with an output schema, the description covers purpose, result nature, and cost, which is sufficient for basic selection and invocation. It lacks any mention of result count or format limitations, but these are not critical given the output schema handles return details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides 100% coverage for parameter q with the description 'Search query'. The tool description does not add any further meaning or usage details about the parameter, so it does not enhance the schema's clarity. A baseline score of 3 is appropriate given the high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Live web search' with the added detail of 'fresh results + short AI answer', which makes the tool's purpose immediately obvious and distinguishes it from siblings like ask_ai or research_report. Although the verb 'search' is implied rather than explicit, the resource (web) is clearly identified.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'fresh results' implies this tool is for time-sensitive or up-to-date information, but there is no explicit guidance on when to use this tool versus alternatives, nor are any exclusions or preconditions mentioned. The usage context is therefore only implied, not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Four image generation tools, three video tools, and five 'ask' tools create significant overlap. Although descriptions specify the model, an agent must carefully compare prices and capabilities to choose correctly, making misselection likely.
All tool names use snake_case, but patterns are mixed: some start with verbs (remove_bg, scrape_page), some with nouns (crypto_prices, market_snapshot), and many use ai_/ask_ prefixes. Model suffixes like flux, gpt, pro, kling are descriptive but not systematically applied.
24 tools is heavy, inflated by near-duplicate variants for image, video, and LLM queries. While the broad scope justifies a large count, the redundant tools could have been consolidated.
The toolset covers a wide range of media and data tasks: image, video, music, voice, vision, LLM, web, crypto, domain, and endpoint discovery. Notable gaps like speech-to-text or image editing exist, but the surface is fairly complete for a general-purpose media toolkit.