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Denue

by io.github.pipeworx-io

Ask Pipeworx — Grounded

ask_pipeworx_grounded
Read-onlyIdempotent

Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,743 across 1500 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confidence, source, fetched_at, refusal_reason:null} on success, OR an explicit refusal {answer:null, refusal_reason:"not_in_source"|"no_tool_match"|"tool_error"|"data_truncated"|"llm_error"} when the data doesn't directly answer. Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for question.
textNoAlias for question.
inputNoAlias for question.
queryNoAlias for question.
promptNoAlias for question.
questionYesYour question in natural language. Accepts query, q, prompt, text, input as aliases.

TDQS

A4.7/5.0
Behavior5/5

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

Goes well beyond the annotations by disclosing the refusal mechanism, the exact return shape, and the failure reason enums. It also states the extra LLM call cost and the constraint of using only tool-result content, none of which are implied by the readOnly/idempotent hints. This is rich behavioral disclosure.

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?

Every sentence earns its place: behavior, return format, refusal cases, usage conditions, and cost comparison are packed into a compact definition. The most important differentiator (grounded, hallucination-resistant) is front-loaded, and there is no redundant filler.

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?

Despite having no output schema, the description fully specifies the success and refusal return shapes, making the response contract clear. It also covers routing, constraints, and alternative selection. For a tool of this complexity, nothing an agent needs to invoke it correctly is missing.

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?

All six parameters are fully documented in the schema as aliases for the same natural-language question field, so schema coverage is 100%. The description adds no parameter-level detail, but none is necessary because the schema already makes the alias structure explicit.

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 identifies this as a hallucination-resistant answer mode for high-stakes reads, distinct from the standard ask_pipeworx tool. It specifies the exact behavior: route, fetch, then extract an answer only from the tool result. This distinguishes it from sibling tools without ambiguity.

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?

Explicit guidance is provided: use when an answer will be quoted, cited, or acted on and facts must not be invented, and prefer ask_pipeworx for casual lookups. It also names the alternative directly and highlights the cost tradeoff, leaving no doubt about when to choose this tool.

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

A4/5.0
Disambiguation4/5

Tools are mostly distinct with detailed descriptions guiding usage, though some overlap exists between similar query tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research, which could cause confusion for agents not reading carefully.

Naming Consistency3/5

All tool names use snake_case and are readable, but there is no consistent verb-noun pattern. Prefixes vary widely (actions like ask, compare, generate vs. domains like denue, polymarket), making the naming scheme inconsistent.

Tool Count3/5

With 35 tools, the count is high for a server named 'Denue', which suggests a narrower focus. While the breadth may be justified by the platform's capabilities, the number feels slightly heavy and could be streamlined.

Completeness4/5

The tool set covers a wide range of data sources and operations, but it is read-only with no write capabilities for external data. Memory and subscription tools add some action, but overall, it is fairly complete for its stated purpose.

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