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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 5,728 across 1498 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 and idempotent annotations, the description discloses the full success and refusal response shapes, the refusal_reason enum, and the rule that only tool-result content is used. This is significant behavioral context, especially given the absence of an output schema.

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 clause earns its place: purpose, routing, extraction rule, return shape, refusal cases, usage guidance, and cost trade-off. It is front-loaded with the core purpose and structured logically.

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 compensates by specifying the exact success and failure response objects. It also covers cost, comparison to ask_pipeworx, and the high-stakes contexts that justify this mode, making the tool actionable without additional lookup.

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?

The input schema covers all six alias parameters with descriptions and notes they are aliases for question, giving 100% schema coverage. The description adds no new parameter semantics, but none are needed since the one meaningful parameter is a natural-language question.

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, distinct from the plain ask_pipeworx sibling, and explains the mechanism: route, fetch, then extract only from the tool result. It states exactly what it returns and when it refuses, making the tool's purpose unmistakable.

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 instructs to use this when answers will be quoted, cited, or acted on, and when the agent must not invent facts. It also names ask_pipeworx as the cheaper alternative for casual lookups, giving a clear when-to-use / when-not-to-use distinction.

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

A3.8/5.0
Disambiguation3/5

The ask_pipeworx family creates real ambiguity: ask_pipeworx_beta explicitly states it currently matches ask_pipeworx exactly, leaving an agent no principled way to choose between them. discover_tools and suggest_questions also overlap as meta-tools for navigating the catalog, though the remaining tools (five polymarket_* tools, entity tools, subscription lifecycle) are well-delineated by their detailed cross-referenced descriptions.

Naming Consistency3/5

The set is uniformly snake_case with coherent subfamilies (ask_pipeworx*, polymarket_*, pipeworx_*, remember/recall/forget), but it mixes imperative verb_phrase names (validate_claim, resolve_entity, generate_llms_txt) with noun_phrase names (entity_profile, recent_changes, ai_visibility_check), and the_committee_convene breaks the pattern entirely with a full-sentence name. The inconsistency is stylistic rather than chaotic, so it stays readable.

Tool Count2/5

At 32 tools, the set exceeds the 25+ threshold for 'too many' and the breadth is not fully earned: ask_pipeworx_beta is self-admittedly redundant right now, and several tools feel bolted on from unrelated domains (the_committee_convene, generate_llms_txt, scan_dependency, ai_visibility_check). The core data-research and prediction-market scope would be tighter and more navigable at roughly 20-24 tools.

Completeness4/5

The primary domain — authoritative data lookup, entity research, and prediction-market analysis — is covered with no dead ends: query (ask_pipeworx, grounded, deep_research), profile (resolve_entity, entity_profile, compare_entities), track (recent_changes), verify (validate_claim), bet research (bet_research, polymarket_edges, arbitrage, fill_risk, kalshi_spread), subscriptions (subscribe/unsubscribe/list/recent_alerts), and memory (remember/recall/forget) form complete lifecycles. Minor gaps exist only at the periphery: no execution layer for prediction-market trades (research stops at fill-risk advice) and single-tool coverage for the npm/llms.txt/AI-visibility side-domains.

Resources