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, idempotentHint, and destructiveHint, so the safety profile is well covered. The description adds that it returns capabilities and usage instructions, but it does not reveal additional behavioral details such as authentication needs, response shape, or whether userId changes behavior.
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 concise and efficient sentences. The first states what the tool returns and the second gives the direct trigger condition. No filler, no repetition of annotations, and the most important interpretational content is front-loaded.
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 tool with an optional userId and no required parameters, the description provides enough information for an agent to select and invoke it correctly. It lacks detail about the output format, but no output schema exists and the purpose is exploratory, so this is a minor gap.
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 only parameter, userId, has full schema description coverage: 'Optional user identifier.' The description adds no extra parameter semantics, but with 100% schema coverage, 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 clearly identifies the purpose: it returns Fodda's main capabilities/features/offerings/tools and how to use them. It also gives a concrete use trigger, which distinguishes it from sibling tools like search_graph, get_node, and get_evidence.
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?
'Call this for any question about what Fodda can do or what's available' explicitly indicates when to use this tool. It does not mention when not to use it or alternatives, but the general capability-discovery nature makes this exclusions less critical.
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 largely distinct, with clear roles for discovery, search, evidence, node detail, neighbor exploration, supplemental data, visualization, and account checks. The main overlap risk is between brand_tracker and search_graph for brand-specific queries, but their descriptions steer usage toward different granularities.
Most tools follow a predictable verb_noun structure such as search_graph, get_node, list_graphs, and check_supplemental_status. The only clear outlier is brand_tracker, which is a noun phrase rather than an imperative verb_noun name.
13 tools cover a complete brand-intelligence workflow without feeling bloated: discovery, search, deep dives, evidence retrieval, supplemental context, visualization, account management, and external URL handling. Each tool has a real role in the overall pipeline.
The tool surface covers the full research journey: list_graphs, search_graph, get_node, get_neighbors, get_evidence, get_supplemental_context, check_supplemental_status, visual generation, and account/capabilities checks. This is a read-only intelligence domain, so the absence of create/update/delete tools is appropriate, not a gap.