AI-powered market research report with trends, key players, and data points. Price: $5.00
| Name | Required | Description | Default |
|---|---|---|---|
| depth | No | Research depth (default standard) | |
| focus | No | Optional focus area | |
| topic | Yes | Market or industry topic to research |
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?
No annotations are provided, so the description carries full responsibility. It discloses the price ($5.00) and that it is AI-powered, but does not reveal whether the operation is read-only, any side effects, rate limits, or response structure. For a paid tool, cost is useful, but other behavioral aspects are absent.
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 one sentence plus a price, which is highly concise. Every word contributes meaning: 'AI-powered,' 'market research report,' 'trends, key players, and data points,' and the price. No filler or redundancy is present.
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?
Given the tool has no output schema and is a paid research report generator, the description does not adequately explain what the report structure looks like or how the 'depth' parameter changes the output. It also lacks usage prerequisites or instructions. While it lists content elements, the overall context is incomplete for an agent to fully understand the tool's behavior.
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 coverage is 100% with each parameter having a description (e.g., 'Research depth (default standard)'), so the baseline is 3. The tool description does not add extra meaning to the parameters, such as how 'depth' affects the report, but it does not need to since the schema already covers them.
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 the tool produces an 'AI-powered market research report with trends, key players, and data points,' which identifies the core function. It distinguishes from siblings like intel_company and deep_research by focusing on market topics, though the verb is implicit rather than explicit.
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 description implies usage for market research via the tool name and mention of 'market research report,' but provides no explicit guidance on when to choose this over alternative research tools. No exclusions or alternatives are mentioned, leaving the agent to infer context from the name alone.
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.
Several tool clusters have near-overlapping purposes: fetch_webpage/fetch_webpage_pro/fetch_resilient and batch_fetch/get_contents are hard to distinguish, and answer_question/research/deep_research differ mainly in price and depth. The search_* and intel_* families are clearer, but the core fetching and research overlap creates ambiguity.
Most tools follow a verb_noun snake_case pattern (fetch_webpage, search_web, extract_data), but there are notable exceptions like domain_intel, package_intel, youtube_transcript, memory_set, and intel_company, where the prefix/suffix convention is inconsistent. Still, the naming is broadly readable.
35 tools is a large surface, far beyond the typical 3-15 range. The server covers many research verticals, but the number feels bloated, especially with multiple fetch and research variants that could be consolidated.
The tool set covers a wide range of web research needs: searching, fetching, crawling, extracting, screenshots, domain/tech/package intelligence, and market/competitive analysis. It lacks obvious lifecycle operations for monitors (list/delete/update) and memory (get/delete), but core workflows are well covered.