research_reportAutomated research: 3 live web searches, then a Claude-written brief with citations and source links. ~$0.10.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Research topic |
| 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. The description adds valuable behavioral context: it performs 3 live web searches, generates a brief via Claude, includes citations and source links, and costs approximately $0.10. No contradiction with annotations.
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 two short, front-loaded phrases that convey the core behavior, output characteristics, and cost without redundancy. Every word 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 multi-step research tool, the description covers the essential behavior (3 searches, AI-written brief, citations, cost). An output schema exists to define the return format, so the description doesn't need to. It lacks only explicit guidance on usage scenarios, but is otherwise complete for its complexity.
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 fully covers the single parameter 'q' with description 'Research topic', providing 100% coverage. The tool description does not add additional meaning, examples, or format details beyond the schema, so baseline 3 is appropriate.
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 identifies this as an automated research tool: it performs 3 live web searches and produces a Claude-written brief with citations and source links. This distinguishes it from siblings like web_search (single search), scrape_page (scraping), and ask_ai (general Q&A).
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?
Usage context is implied: it is for research requiring multiple web searches and a synthesized brief. However, the description provides no explicit when-to-use vs alternatives or exclusions, such as when a single web search or ask_ai would be more appropriate.
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.
Most tools have clearly distinct purposes (image, music, video, vision, voice, etc.), but some overlap exists: ask_ai vs ask_ai_pro differ only in model strength, and web_search vs research_report both involve search with AI responses. Descriptions help clarify, though an agent could misselect in edge cases.
Tool names follow a mostly consistent snake_case pattern, with many using an 'ai_' prefix for generation tasks. However, name styles vary between verb_noun (call_endpoint, remove_bg) and noun_verb (crypto_prices, domain_info), and ask_ai/ask_ai_pro break the ai_ prefix convention. Minor deviations, but the overall pattern is readable.
At 16 tools, the server is slightly above the ideal 3-15 range but remains well-scoped for a multi-purpose utility server. Each tool has a distinct function, and the count feels manageable rather than overwhelming.
The server covers a broad set of capabilities (AI generation, web search, crypto, domain info), but it lacks lifecycle management for generated assets—there are no list/get/delete operations for previously created media, and the domain appears to be a collection of paid endpoints rather than a cohesive service.