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groundcheck_evaluate_faithfulness

Read-only

Score how well a RAG-generated answer is supported by provided source chunks, claim by claim, returning a faithfulness score, claim counts, and verdicts for unsupported or contradicted claims.

Instructions

Score how well answer is supported by sources, claim by claim.

Use this when you need a faithfulness score (0-1) and want to see every
claim's verdict, not just the problems -- for that, use
groundcheck_detect_hallucinations instead, which is tighter and cheaper.

Args:
    answer: the RAG-generated answer text to check.
    sources: list of {id, text} chunks the answer was generated from,
        e.g. [{"id": "doc1#chunk3", "text": "..."}].
    response_format: "concise" (default) returns the score, claim counts,
        and only unsupported/contradicted claims. "detailed" returns
        every claim's verdict.

Returns a score (supported/total claims), counts by verdict, and a claims
list (filtered per response_format). Costs 2 model calls via your client's
sampling (decompose, then verify) -- no API key needed if your client
supports sampling.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
answerYes
sourcesYes
response_formatNoconcise

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
scoreYessupported claims / total claims, 0-1.
claimsYesConcise: only problem claims. Detailed: every claim.
supportedYes
unsupportedYes
contradictedYes
total_claimsYes
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=false, destructiveHint=false. The description adds valuable context: costs 2 model calls via sampling, no API key needed if client supports sampling, and explains response format behavior (concise vs detailed). 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.

Conciseness4/5

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

The description is well-structured with a clear purpose sentence, usage guidance, then args list. It is slightly long but every part adds value. Front-loading the key purpose and sibling differentiation is effective.

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 3 parameters, an output schema is referenced (not shown), and description covers return structure (score, counts, claims) and cost. It lacks details like max sources limit or async behavior, but overall is quite complete for a metadata evaluation tool.

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%, so description carries full burden. It explains 'answer' as RAG-generated answer text, 'sources' as list of {id, text} chunks, and 'response_format' with values 'concise' (default) and 'detailed' plus their return behavior. This adds significant meaning beyond bare schema.

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 tool description explicitly states it scores faithfulness of answer vs sources claim-by-claim. It clearly distinguishes from the sibling tool groundcheck_detect_hallucinations by noting it shows every claim's verdict instead of just problems.

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?

The description gives explicit when-to-use (need overall faithfulness score with all verdicts) and when-not-to-use (use groundcheck_detect_hallucinations for tighter/cheaper problem-only detection). This is excellent guidance with named alternative.

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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