Skip to main content
Glama

Fodda Topic & Trend Research

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?

Annotations already communicate read-only, idempotent, non-destructive behavior. The description adds useful context by listing what the response contains (lifecycle stage, signal strength, geographic scope, all properties) and by highlighting the external dependency on a previously obtained node_id. This goes beyond the structured annotations without contradicting them.

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 a compact three-sentence structure that front-loads the core purpose and return content, then provides the usage condition and dependency. No wasted words or repeated schema details.

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?

For a simple read-only node retrieval with no output schema, the description adequately explains the return categories and the required prior context. It does not cover failure or edge-case behavior, but the schema handles node_id constraints well, so an agent has enough information to call the tool correctly.

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% and the schema itself provides detailed parameter guidance, including strong warnings against guessing node IDs and example graph IDs. The description mostly restates the node_id dependency already present in the schema, so it adds little new parameter meaning.

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?

Clearly specifies a single verb ('Get'), a resource (a specific trend/node), and the type of payload returned: full profile including description, lifecycle stage, signal strength, geographic scope, and all properties. It also frames itself as a deeper follow-up to search_graph, which distinguishes it from the main sibling likely to be confused with it.

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?

Tells the agent when to use it ('when you need deeper detail on a single trend after search_graph returned a summary') and states a required precondition: node_id must come from a prior search_graph result. It could be stronger by explicitly naming related alternatives like get_neighbors or get_evidence and saying when not to use them.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.3/5.0
Disambiguation4/5

The tools are mostly distinct: search_graph, search_insights, search_statistics, and get_supplemental_context are carefully differentiated by output type, and graph exploration/evidence tools have clear roles. A couple of retrieval tools (get_validated_trends vs search_graph, search_statistics vs get_supplemental_context) could be mistaken at a glance, though their descriptions do enough to separate them.

Naming Consistency5/5

All tool names use a consistent lowercase snake_case verb_noun pattern (search_*, get_*, list_*, generate_*, check_*, read_*). Verbs map predictably to actions, and there are no mixed conventions or vague generic names.

Tool Count5/5

Fifteen tools is at the upper edge of the ideal range but each one maps to a distinct research workflow step: discovery, graph search, targeted retrieval, evidence, supplemental data, visualization, and account/capability checks. The breadth is justified by the server's broad trend-research scope.

Completeness5/5

The surface covers the full read-only research lifecycle: list graphs, search across them, drill into nodes/neighbors/evidence, get quantitative and qualitative answers, supplement thin coverage with external data, and produce visuals. Meta tools for account/capability and URL import prevent dead ends.