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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 5,714 across 1495 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?

Beyond the readOnly/idempotent annotations, the description discloses the full refusal behavior, refusal_reason taxonomy, the guarantee to use only tool result content, and the exact success/refusal return shapes. It also notes the additional LLM call cost and the high-stakes framing.

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 long but every sentence earns its place: core identity is front-loaded, followed by behavior, refusal modes, use cases, and cost trade-off. No filler or redundancy.

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?

With no output schema, the description correctly takes on the burden of explaining return values and refusal modes. It also covers routing context, source scope, use cases, and the cost-vs-accuracy trade-off, making the definition complete for correct invocation.

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 all six parameters are aliases for the question parameter, so the schema fully documents them. The description adds context about routing and internal argument filling, but it does not add meaning beyond what the schema already provides.

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 states a specific purpose: a hallucination-resistant answer mode for high-stakes reads, with the same routing as ask_pipeworx but grounded extraction. This clearly distinguishes it from the sibling ask_pipeworx and explains exactly what the tool does.

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?

Explicitly states when to use it (answers will be quoted, cited, or acted on; must not invent facts) and when not to (prefer ask_pipeworx for casual lookups). It also discloses the extra cost of one LLM call, giving clear selection criteria.

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

A4.3/5.0
Disambiguation5/5

Each tool has a clearly defined, distinct purpose. Even closely related tools like ask_pipeworx and ask_pipeworx_grounded are differentiated by use case (casual vs. high-stakes), and the prediction market tools each cover a specific function (research, edge detection, arbitrage, fill risk, tracking, cross-venue spreads). Detailed descriptions eliminate ambiguity.

Naming Consistency4/5

Tool names predominantly follow a verb_noun pattern (e.g., ask_pipeworx, compare_entities, resolve_entity), but a few use noun_noun or adjective_noun forms (e.g., entity_profile, recent_alerts). The naming is generally predictable and readable, with minor deviations from a strict pattern.

Tool Count3/5

The server offers 32 tools, which is above the typical 3-15 range for a well-scoped server. However, the vast domain (financials, prediction markets, news, memory, subscriptions, etc.) justifies the count. It is on the heavy side but still manageable with clear organization.

Completeness5/5

The tool surface is remarkably complete for the apparent domain: exploration (discover_tools, suggest_questions), identifier resolution (resolve_entity), data retrieval (ask_pipeworx, deep_research, entity_profile, compare_entities, recent_changes, validate_claim), prediction market analysis (full suite), memory (remember/recall/forget), subscriptions (subscribe/unsubscribe/list/recent_alerts), and extras (ENS, dependency scan, AI visibility). No obvious gaps exist.

Resources