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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 5,718 across 1496 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?

Annotations already cover readOnly, openWorld, idempotent, and non-destructive hints. The description goes well beyond: it details the extraction process, the structured success response, the explicit refusal reasons, and the cost implication. None of this contradicts annotations, and it discloses behaviors an agent needs to handle refusal scenarios.

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?

The description is dense but every sentence earns its place: it front-loads the purpose, then the mechanism, return format, use cases, and cost trade-off. It is structured to be scanned quickly, with no fluff or repetition.

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 is complex—it routes, extracts, and refuses—and the description covers all necessary aspects: the return object with evidence and refusal reasons, the cost side-effect, and when to prefer the cheaper sibling. No output schema exists, so the description adequately compensates by specifying the exact return shape.

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% with all six parameters being aliases for a single question field. The description adds nothing beyond what the schema states; it simply refers to 'question' in the return structure. Since the schema fully documents parameter semantics, a baseline of 3 is appropriate.

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 opens with a precise purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It clearly identifies the verb (answer), resource (Pipeworx data), and the distinguishing behavior (extraction only from tool results). It explicitly names its sibling ask_pipeworx to differentiate, leaving no 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?

It gives explicit when-to-use ('whenever an answer will be quoted, cited, or acted on') and when-not-to ('prefer ask_pipeworx for casual lookups'), directly comparing to the sibling tool. It also quantifies the trade-off (one extra LLM call), enabling an informed decision.

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

A3.7/5.0
Disambiguation3/5

Many tools have distinct purposes, but there are multiple ask_pipeworx variants and several Polymarket tools with similar functions, causing potential confusion. Most other tools are clearly differentiated, but the overlap in query and betting tools reduces clarity.

Naming Consistency2/5

Naming conventions are inconsistent: some tools use snake_case (ai_visibility_check), others use descriptive phrases (ask_pipeworx, generate_llms_txt), and some are very short (arrivals). Lengths vary widely, and there is no uniform pattern.

Tool Count2/5

37 tools is excessive for a coherent server; the scope is too broad, spanning transport, data lookups, prediction markets, and utilities. This suggests a lack of focus, making the server feel like a bundled collection rather than a well-scoped toolkit.

Completeness3/5

The server covers multiple domains but lacks depth. London transport tools are partial (e.g., no real-time tube positions), and domains like weather or stock quotes rely on the ask_pipeworx meta-tool rather than dedicated tools. The surface is broad but not comprehensively complete in any area.

Resources