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 convey that the tool is readOnly, idempotent, and non-destructive, so extra credit is given for any increment beyond that. The description adds that the tool returns 'how to use them' along with the capabilities list, but it does not explain any behavior around the optional userId, response shape, or conditions under which the listing might be server-driven or personalized. It is not contradictory and does add some content scope, but it is a baseline behavioral disclosure.
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 sentences and frontloads the core return value, then adds usage guidance. The list 'capabilities / features / offerings / products / services / tools' is somewhat redundant, but not wasteful enough to derail clarity. It is concise and well-structured.
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, idempotent capability-list tool with one optional parameter and no output schema, the description is nearly complete. It lacks any mention of what userId does, which is a small but relevant gap since the parameter exists in the schema. Still, an agent has most of what it needs: purpose, return scope, and usage guidance.
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%: the only parameter is 'userId', described as an optional user identifier. The description mentions no parameter behavior, so it adds nothing beyond the schema. Since this meets the high-coverage baseline, a 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') and resource ('Fodda's main capabilities / features / offerings / products / services / tools and how to use them'). It clearly conveys the purpose of the tool and is easily distinguished from sibling tools, none of which serve the same broad capability-discovery function.
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 second sentence gives explicit guidance: 'Call this for any question about what Fodda can do or what's available.' This defines clear usage context for an agent. However, it does not mention alternatives or when not to use this tool, so it falls short of the full 'when/when-not/alternatives' standard.
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 roles: graph discovery, graph search, node detail, neighbor mapping, evidence retrieval, deep research launch, and status polling are all identifiable. The main ambiguity is between search_graph and get_label_values(label='Trend') for listing trends, and between deep_research_topic's built-in supplemental coverage and get_supplemental_context.
The set largely follows a snake_case verb_noun pattern such as search_graph, list_graphs, get_node, read_url, and generate_visual. The outlier is deep_research_topic, which is noun-led rather than verb-led, and get_my_account is a minor deviation from the pure verb_noun pattern, but neither seriously disrupts usability.
14 tools is well-scoped for a deep-research platform: graph discovery, graph search, retrieval, evidence, supplemental data, status polling, visualization, URL ingestion, and account/capability helpers all earn their place. The set feels like a deliberate pipeline rather than a miscellany.
The research lifecycle is well covered: discover graphs, search, explore trends, get supporting evidence, add supplemental data, launch deep research, poll status, and generate visuals. Minor conveniences like canceling or listing past research sessions are missing, but there are no dead ends for the core workflow.