Returns Fodda's main capabilities / features / offerings / products / services / tools and how to use them. Call this for any question about what Fodda can do or what's available.
| Name | Required | Description | Default |
|---|---|---|---|
| userId | No | Optional user identifier. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds that the tool returns with usage instructions, which is helpful context but does not substantially go beyond what annotations and the overall intent reveal.
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 concise at two sentences, front-loads the core action, and avoids redundant restatement of the tool name. The slash-separated list is somewhat verbose but helps capture the breadth without adding real bloat.
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, read-only, optional-parameter tool with no output schema, the description provides enough guidance on when to call it and what it returns. It could mention the return format or the role of userId, but the existing context is sufficient for an agent to decide and execute.
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 userId fully described as 'Optional user identifier.' The description does not explain why or how userId should be used, but the schema handles the parameter semantics, so the baseline of 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 states a specific verb ('Returns'), a clear resource ('Fodda's main capabilities / features / offerings / products / services / tools'), and adds the purpose 'and how to use them.' This makes the tool's function immediately identifiable and distinguishes it from sibling data-retrieval tools like get_node or search_graph.
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 gives explicit guidance: 'Call this for any question about what Fodda can do or what's available.' This is a clear when-to-use directive, though it does not explicitly state when not to use it or point to alternatives.
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.
The tools are mostly distinct: search_graph, search_insights, search_statistics, and get_supplemental_context are carefully differentiated by output type, and graph exploration/evidence tools have clear roles. A couple of retrieval tools (get_validated_trends vs search_graph, search_statistics vs get_supplemental_context) could be mistaken at a glance, though their descriptions do enough to separate them.
All tool names use a consistent lowercase snake_case verb_noun pattern (search_*, get_*, list_*, generate_*, check_*, read_*). Verbs map predictably to actions, and there are no mixed conventions or vague generic names.
Fifteen tools is at the upper edge of the ideal range but each one maps to a distinct research workflow step: discovery, graph search, targeted retrieval, evidence, supplemental data, visualization, and account/capability checks. The breadth is justified by the server's broad trend-research scope.
The surface covers the full read-only research lifecycle: list graphs, search across them, drill into nodes/neighbors/evidence, get quantitative and qualitative answers, supplement thin coverage with external data, and produce visuals. Meta tools for account/capability and URL import prevent dead ends.