get_nodeGet full details for a specific Gonka documentation concept by name.
| Name | Required | Description | Default |
|---|---|---|---|
| label | Yes | Concept name (or a close substring of it), e.g. "collateral" or "escrow deposit". Use query_graph() first if you don't already know the exact concept name. |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / label / descriptionAdded value: +"Concept name (or a close substring of it), e.g. \"collateral\"\nor \"escrow deposit\". Use query_graph() first if you don't\nalready know the exact concept name."Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is clear. The description adds no extra behavioral context (e.g., what 'full details' includes, behavior on missing concept). It does not contradict 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?
The description is a single sentence, front-loaded with the verb and resource, and contains no unnecessary words. It is appropriately concise for a simple lookup tool.
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 one-parameter, read-only, idempotent tool with an output schema, the description is completely sufficient. The agent knows what it does and can rely on the schema and annotations for details.
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%, with a thorough parameter description for 'label' including examples and guidance to use query_graph first. The tool description adds no additional meaning beyond the schema, so baseline 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 ('Get full details') and resource ('Gonka documentation concept') with a clear method ('by name'). This distinguishes it from siblings like query_graph (which searches/finds nodes) and read_doc (which reads a document page). It is unambiguous about what the tool does.
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 implies usage when you have a specific concept name and need full details, but it does not explicitly state when to use it versus alternatives. The query_graph() guidance appears only in the schema's parameter description, not in the main description, so it is not credited here per the 'beyond structured fields' principle.
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 distinct purposes, but there is slight overlap between register_on_gonka and get_signup_link (both return signup links) and between get_pricing, calculate_savings, compare_providers, and suggest_model_for_task (all deal with pricing). Descriptions help differentiate, but boundaries are not perfectly sharp.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., calculate_savings, get_available_models, search_docs). No mixed conventions or abbreviations. Naming is predictable and clear.
18 tools is on the higher side, but each serves a specific function within pricing, documentation, and signup workflows. The count feels slightly bloated, particularly with closely related documentation graph tools, but overall still well-scoped.
The tool set comprehensively covers the domain of Gonka Network pricing: live pricing, comparisons, savings calculations, model recommendations, documentation search (graph and full-text), trial key provisioning, and signup links. No obvious gaps for the stated purpose.