Check the current user's account status: API call balance, plan, enabled/disabled graphs, and profile info. Use when the user asks "how many API calls do I have?", "what plan am I on?", "what graphs can I access?", or similar account questions. Returns live data — not cached from session start.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true and destructiveHint=false. The description adds meaningful behavioral context beyond annotations by stating 'Returns live data — not cached from session start', which clarifies data freshness. No contradictions with 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?
Three concise sentences: first states what the tool does and what it returns, second gives usage examples, third adds a behavioral note. Every sentence earns its place with no redundant or filler content.
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 parameterless, read-only tool with good annotations, the description covers all essential aspects: purpose, usage triggers, behavioral characteristic (live data). No missing elements given the simplicity of the tool.
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 tool has zero parameters, so schema coverage is trivially 100%. Per guidelines, baseline is 4 when there are no parameters. The description does not need to add parameter information and correctly focuses on the tool's purpose.
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 uses the verb 'Check' with a specific resource ('current user's account status'), enumerates exact fields (API call balance, plan, graphs, profile info), and naturally distinguishes from all listed sibling tools which serve different purposes (e.g., get_capabilities, check_supplemental_status).
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 provides explicit example queries ('how many API calls do I have?', 'what plan am I on?') that clearly indicate when to use this tool. It does not explicitly mention when not to use it or suggest alternatives, but the context is well-defined and sufficient given that sibling tools have clearly distinct purposes.
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.