research_topicResearch any topic to get AI-generated insights, sources, and a content outline, useful before writing an article.
Research a topic and get AI-generated insights, sources, and a content outline. Useful before writing an article.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Research topic or question. Be specific for best results. |
Changes observed during successful MCP inspections.
v5.1.3Input schema / properties / query / descriptionPrevious value: -"The topic or question to research, 5-500 characters. Specific beats broad: 'how small SaaS teams price annual plans' returns more than 'pricing'."New value: +"Research topic or question. Be specific for best results."v5.1.0Input schema / properties / query / descriptionPrevious value: -"Research topic or question. Be specific for best results."New value: +"The topic or question to research, 5-500 characters. Specific beats broad: 'how small SaaS teams price annual plans' returns more than 'pricing'."v2.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=false, and destructiveHint=false, so safety and side-effect profile are covered. The description adds that the output is AI-generated and includes sources and an outline, which is modest but genuine behavioral context beyond the 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?
Two short sentences with the core purpose front-loaded and no filler. Nothing repeated from the schema or annotations, though it stops short of being maximally informative per word.
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?
There is no output schema, so the description must describe returns, and it does so by naming insights, sources, and a content outline. Combined with annotations covering safety, this is nearly complete for a single-parameter tool, with only minor gaps in result format depth.
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?
Schema description coverage is 100%, with the single 'query' parameter fully documented in the schema (including the 'be specific' guidance and length bounds). The description adds no parameter syntax or format meaning, so the baseline 3 for full schema coverage applies.
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 pairs a specific verb (Research) with a resource (a topic) and enumerates the outputs: AI-generated insights, sources, and a content outline. This is clear and concrete, though it does not explicitly contrast itself against content-generation siblings like generate_title_seo or suggest_titles.
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?
"Useful before writing an article" gives an implied workflow position, so the agent can infer the stage at which to call it. However, no alternatives are named and no when-not conditions are stated, leaving usage guidance at an implied level.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.