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Gonka Second Opinion (multi-model)

by io.github.bystray

Query Documentation Graph

query_graph
Read-onlyIdempotent

Search Gonka documentation. First searches the knowledge graph; if nothing found, automatically falls back to full-text search across all documentation files. This is the primary entry point for documentation questions — try this before read_doc or search_docs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNoHow many hops to traverse from the matched concept in the knowledge graph. Higher = more context, more tokens.
questionYesNatural-language question or topic, e.g. "how do I deposit GNK".
token_budgetNoApproximate max size of the returned text.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the readOnlyHint and idempotentHint annotations, the description reveals valuable behavioral logic: first searches the knowledge graph, then automatically falls back to full-text search if nothing found. This exposes the tool's search strategy and fallback behavior, which is not inferable from annotations alone.

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?

Two concise sentences: first states purpose and fallback behavior, second gives usage guidance. Every sentence earns its place and the most critical information is front-loaded.

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

Completeness5/5

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

The tool is well-situated among siblings: it names read_doc and search_docs, explains the search algorithm, and the output schema covers return values. Given the annotations and schema richness, the description is complete for an agent to select and invoke this 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?

The input schema already provides complete descriptions for all three parameters (question, depth, token_budget), with 100% coverage. The description does not add additional parameter-level meaning, so it meets the baseline but does not exceed it.

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 'Search Gonka documentation' with a specific verb and resource. It also explicitly frames the tool as the primary entry point, distinguishing it from read_doc and search_docs by naming them directly.

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

Usage Guidelines5/5

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

Gives explicit directive: 'try this before read_doc or search_docs.' This is clear when-to-use guidance and names alternatives, making the decision process unambiguous.

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

A4.1/5.0
Disambiguation4/5

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.

Naming Consistency5/5

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.

Tool Count4/5

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.

Completeness5/5

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.

Resources