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get_answer

Answer how, where, or why coding questions with evidence-grounded responses, including confidence levels and verifiable evidence for reliable codebase understanding.

Instructions

Answer a how, where, or why question in one evidence-grounded call.

High confidence is content-grounded and may be used directly. Medium
confidence keeps the smallest verification evidence; low confidence leads
with an actionable local conclusion and ranked evidence. Provider keys and
network access are optional: local source, symbols, FTS, rationale, and
data-shape evidence remain usable when embeddings or synthesis fail.

Responses fit 24,000 serialized characters. Pass ``include=["evidence"]``
for the deduplicated expanded projection, capped at 32,000. Reductions carry
totals, emitted counts, reasons, and an exact one-call recovery.

Args:
    question: Developer question.
    scope: Optional repository-relative path prefix.
    repo: Usually omitted; a workspace alias when needed.
    include: Optional ``["evidence"]`` expanded projection.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repoNo
scopeNo
includeNo
questionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Install Server

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden and does so thoroughly. It discloses confidence tiers, evidence grounding, optional provider keys/network, fallback behavior, response size caps, reduction details, and recovery semantics. This is far beyond a basic 'answers questions' statement.

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?

Although long, every sentence earns its place: purpose, confidence behavior, offline fallbacks, size limits, reduction recovery, and parameter semantics. The description is front-loaded with the core purpose and organized with clear sections; nothing is wasted.

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 has 4 parameters, no annotations, and an output schema. The description covers purpose, usage scope, param meaning, behavioral guarantees, failure/recovery paths, and output constraints. There is no obvious missing information an agent needs to select and invoke it correctly.

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

Parameters5/5

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

Schema description coverage is 0%, yet the description's Args section adds meaningful semantics for every parameter: 'question: Developer question', 'scope: Optional repository-relative path prefix', 'repo: Usually omitted; a workspace alias when needed', and 'include: Optional ["evidence"] expanded projection'. This compensates fully for the absent schema descriptions.

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 states a specific verb and resource: 'Answer a how, where, or why question in one evidence-grounded call.' This clearly distinguishes the tool from siblings like get_health or get_risk, which target different query types. The phrase 'how, where, or why' anchors exactly when this tool applies.

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?

It gives clear context on when to use the tool for how/where/why questions, and it excludes general change-risk/health/overview concerns implicitly. It does not explicitly name sibling alternatives or state 'when not to use this vs. X', but the question-type framing is strong enough to guide selection.

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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