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Fodda Synthetic Expert Consult

get_node

Read-onlyIdempotent

Get the full profile of a specific trend — detailed description, lifecycle stage (emerging/building/mature), signal strength, geographic scope, and all properties. Use when you need deeper detail on a single trend after search_graph returned a summary. Requires node_id from a prior search_graph result.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nodeIdYesThe node_id from a prior search_graph result (e.g. '2507.0'). MUST come from the search result's node_id field. Node IDs are NOT sequential integers — do NOT guess or invent IDs like '1', '2', '3'. Do NOT pass the trend name.
userIdNoOptional user identifier for trial usage tracking.
graphIdYesThe graph ID. Use list_graphs to see all options. Examples: 'retail', 'tech', 'food', 'travel', 'beauty', 'sports', 'sic', 'pew', 'ce-design', 'ezra-eeman-wayfinder', 'dhl-ecommerce-trends-2026', 'automotive-color-trends', 'alyson-stevens-macro', 'dentsu-creative-marketing', 'pwc/sxsw-2026-key-insights', 'green-house/thrive-report', 'michaels-2026-creativity-trend-report', 'delta/the-connection-index'

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With annotations declaring readOnlyHint, idempotentHint, and destructiveHint false, the safety profile is already clear. The description adds valuable context about the return fields and the provenance requirement for node_id, explaining that IDs must come from search_graph and not be guessed. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three concise sentences with no filler: the first lists the return fields, the second gives the usage context, and the third states the key prerequisite. Each sentence earns its place and information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has no output schema, the description compensates by listing the main return fields and noting 'all properties'. It also provides the critical usage context and prerequisite. However, it doesn't describe the exact output structure or possible failures, which would be helpful but is not a major gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, including nodeId, nodeId with anti-guessing guidance, graphId with list_graphs reference and examples, and userId. The description provides no additional parameter semantics beyond what the schema already defines, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Get' and the resource 'full profile of a specific trend', then enumerates the key attributes: detailed description, lifecycle stage, signal strength, geographic scope, and all properties. It also differentiates from search_graph by noting it is for deeper detail on a single trend, making the tool's purpose unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says to use it when deeper detail is needed 'after search_graph returned a summary' and requires a node_id from a prior search_graph result. This gives clear context and a prerequisite, though it doesn't explicitly state when not to use the tool or list alternative siblings beyond search_graph.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.4/5.0
Disambiguation4/5

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.

Naming Consistency5/5

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.

Tool Count5/5

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.

Completeness5/5

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.