Lists available human agents and synthetic analysts (e.g. brand-cmo, brand-ceo, brand-cfo, human experts like Anu Lingala). To query a company-specific synthetic expert (e.g., "Nike CMO", "Apple CMO", "Adidas CEO"), consult brand-cmo (or relevant role ID) and supply the target company name in the company parameter (e.g. company: "Nike").
| 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, destructiveHint=false, covering the safety profile. The description adds meaningful context beyond annotations by specifying that the tool lists both human agents and synthetic analysts, clarifying the scope of outputs. It does not describe return format or pagination, but the annotation coverage lowers the burden.
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, front-loaded with the primary purpose in the first sentence. The second sentence adds usage guidance without redundancy. Every phrase earns its place, making it highly 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 list tool with one optional parameter and no output schema, the description adequately covers the tool's function and distinguishes it from querying. It gives enough context for an agent to select and invoke the tool correctly, including examples of expected content.
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 input schema has one optional parameter 'userId' with a description, giving 100% schema coverage. The tool description does not mention this parameter, but the schema fully documents it. Per the baseline, with high schema coverage the description need not add parameter details, so 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 uses a specific verb 'Lists' and clearly identifies the resource: available human agents and synthetic analysts. It provides concrete examples (e.g., brand-cmo, brand-ceo, Anu Lingala), which disambiguates the tool from siblings like consult_analyst. The purpose is unambiguous.
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 explicitly tells when to use this tool (to list analysts) and when not to: 'To query a company-specific synthetic expert... consult brand-cmo' implies using a different tool (likely consult_analyst). It gives a clear alternative path, though it does not name the sibling tool directly, which prevents a 5.
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, get_node, get_neighbors, get_evidence, and get_label_values all relate to graph exploration, but each has a clear role (search vs. profile vs. relationships vs. evidence vs. label enumeration). consult_analyst and consult_human_agent are similar in purpose and wording, but the 'Synthetic' vs. 'Human Agent' distinction in names and descriptions is sufficient to keep them separated.
All tools follow a consistent snake_case verb_noun pattern, with sensible verbs like get_, list_, search_, request_, check_, consult_, and generate_. The paired request_deliverable / check_deliverable_status and list_analysts / consult_* relationships are clear and predictable.
14 tools is well within the ideal 3–15 range and each tool appears justified: graph discovery, trend exploration, evidence retrieval, expert consultation, deliverable commissioning, visual generation, account/capability introspection, and analyst listing. No redundant extras or obvious bloat.
The tool set covers the full workflow promised by the server: discovering graphs and analysts, searching and drilling into trends, retrieving evidence, consulting experts, commissioning and polling deliverables, generating visuals, and checking account status. There are no significant dead ends or clearly missing operations for the stated expert-consultation purpose.