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zippopotam

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?

The description reveals key behaviors beyond the annotations: it only uses the tool result, can return explicit refusals with detailed reasons, and costs an extra LLM call relative to ask_pipeworx. It does not contradict the readOnlyHint, openWorldHint, or idempotentHint annotations.

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: purpose, routing, extraction behavior, return contract, refusal reasons, usage guidance, and cost tradeoff. The most important scoping statement is front-loaded.

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?

Even without an output schema, the description defines the full success/refusal response shapes and enumerates all refusal_reason values. Combined with the annotations and schema, an agent has everything needed to decide when and how to invoke this tool.

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 documented as aliases for 'question'. The description adds no additional parameter-level meaning, but it doesn't need to because the schema already fully explains the single semantic parameter.

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 clear, specific purpose: a hallucination-resistant variant that extracts answers only from tool results, explicitly contrasting itself with ask_pipeworx. This differentiates it from the sibling tool without requiring schema inspection.

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 guidance: whenever an answer will be quoted, cited, or acted on and facts must not be invented. It also names the alternative (ask_pipeworx) and says to prefer that for casual lookups due to the extra LLM call.

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
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, and bet_research all route questions to the same underlying engine, and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. The polymarket_* family also has many closely-related entry points, though descriptions do help differentiate them.

Naming Consistency4/5

Names consistently use snake_case with descriptive verb-first patterns (ask_, lookup_, scan_, validate_, resolve_, subscribe) and a clear polymarket_ family prefix. Minor inconsistency exists between lookup_city/lookup_zipcode and resolve_entity, and between noun-style names like entity_profile vs verb-style names like compare_entities, but the overall style is predictable.

Tool Count2/5

33 tools is heavy for a single server, and multiple could be consolidated: the ask_pipeworx variants and deep_research largely overlap, and the memory/subscription categories could be collapsed. For a data-platform gateway the breadth is arguably justified, but the visible redundancy makes the surface feel bloated rather than well-scoped.

Completeness4/5

The core domains are well covered: question answering has multiple modes, entity lookup has resolution and profiling, prediction markets have research/edge/arb/fill-risk coverage, and the memory (remember/recall/forget) and subscription (subscribe/list/unsubscribe/recent_alerts) lifecycles are complete. Minor gaps exist such as no direct tool to fetch a pipeworx:// record by URI, relying instead on MCP resources.

Resources