check_groundednessHallucination check: is a claim actually supported by a source text?
Brainiall Groundedness engine. Returns {grounded, confidence, supporting_span, reason}.
| Name | Required | Description | Default |
|---|---|---|---|
| claim | Yes | The claim to verify | |
| source | Yes | The source text the claim should be grounded in |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish that this is read-only, idempotent, and non-destructive. The description adds useful behavioral context by specifying the return payload—grounded, confidence, supporting_span, reason—which is especially valuable because no output schema is present.
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 compact and front-loaded: the first sentence states the purpose directly, and the second provides the return format. There is no filler or duplication of schema fields.
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 two-parameter, read-only tool with strong annotations and no output schema, the description is sufficient. It explains what the tool does, what inputs are expected, and what the response will contain, making it complete for invocation purposes.
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%, so both claim and source are already documented with clear meanings. The description mostly restates these concepts instead of adding new parameter-level detail, so it does not elevate the semantics beyond the schema.
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 clearly identifies the tool's core operation: verifying whether a claim is supported by a given source text, framed as a hallucination check. It is specific and distinct from general text analysis siblings, though it does not explicitly name or differentiate itself from alternatives.
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 usage context is implied through the 'claim vs source text' framing, but there is no explicit guidance on when to choose this tool over related siblings like answer_question or knowledge_query. No exclusions or alternative routing are provided, so the agent must infer applicability.
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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Most tools map cleanly to distinct capabilities, and the descriptions make the intended use clear. A few adjacent pairs—extract_entities vs link_entities_to_wikidata and detect_pii vs detect_conversational_pii—require careful selection, but they are distinguishable by their stated outputs.
Names are uniformly lowercase snake_case and mostly follow a verb_object pattern, such as analyze_*, detect_*, extract_*, summarize_text, and translate_text. A few outliers like aspect_sentiment, fraud_feedback, and knowledge_ingest break the verb-first feel, but the overall pattern remains predictable.
At 22 tools, this is on the heavy side of the borderline range. Each tool has a distinct job, but the mix of core NLP, safety, fraud, health-checking, and knowledge-base management makes the surface feel sprawling rather than tightly scoped.
The core NLP coverage is broad: sentiment, toxicity, PII, entities, QA, summarization, translation, and groundedness are all present. However, the knowledge-base tools support ingest/list/query but no delete or update, creating a dead end when documents need correction or removal.