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Brainiall NLP

by com.brainiall

Answer Question About Text

answer_question
Read-onlyIdempotent

Answer a question using ONLY the supplied text; returns the supporting sentence(s) with character offsets.

Replies found:false rather than guessing when the answer isn't present in the text.

Args: text: The text/document to answer from. question: The question to answer.

Returns: dict with keys: answer (str|null), found (bool), supporting_spans (list of {text, start, end}).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe text/document to answer from
questionYesThe question to answer

TDQS

A3.5/5.0
Behavior1/5

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

Annotations set openWorldHint=true, while the description explicitly restricts the tool to 'ONLY the supplied text' and promises 'found:false rather than guessing.' This is a direct contradiction with the annotations, triggering the annotation-contradiction rule despite otherwise useful behavioral details such as supporting spans and null answers.

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 key constraint is front-loaded in one sentence, and the Args/Returns layout is easy to scan. Some redundancy with the schema's parameter descriptions is minor and does not harm readability.

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?

For a two-parameter read-only tool with no output schema, it supplies the return dictionary shape, the no-guess behavior, and character-offset details, which is nearly complete. The only notable gap is reconciling the conflicting openWorldHint annotation with the closed-text claim.

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?

Schema coverage is 100% and the Args section mostly repeats the schema descriptions. No significant additional meaning is added beyond 'text' being the document and 'question' the query, so the baseline score of 3 applies.

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?

States a specific verb ('answer'), scope ('using ONLY the supplied text'), and what is returned (supporting sentence(s) with character offsets). This clearly separates it from sibling tools like knowledge_query or summarize_text.

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?

The phrase 'using ONLY the supplied text' and 'Replies found:false rather than guessing' define clear usage context for grounded extractive QA. However, it never names a sibling alternative or states when not to use other text tools, so no explicit routing is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Brainiall NLP toolsInspect
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TDQS

A3.6/5.0
Disambiguation4/5

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.

Naming Consistency4/5

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.

Tool Count3/5

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.

Completeness3/5

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.

Resources