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Uk Food Hygiene

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 goes well beyond the annotations by disclosing the exact success return shape, the refusal return shape, the specific refusal reasons, the extra LLM call cost, and the constraint that answers come only from tool results. The annotations (readOnlyHint, idempotentHint, openWorldHint) are consistent with this read-only, non-destructive behavior, and the description adds valuable detail they don't cover.

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 covers purpose, routing mechanism, return contract, refusal behavior, usage context, and the cost comparison with ask_pipeworx. The critical differentiator is front-loaded, and the practical guidance comes at the end without 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 present, the description must explain return values, and it does: success objects, refusal objects, and refusal reason enums are enumerated. It also covers cost, routing, and when to choose an alternative, making the tool fully invocable and interpretable by an agent.

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% because all six parameters are aliases for the same 'question' field, and each parameter already has a description in the schema. The tool description does not add much parameter-level nuance beyond implying a natural-language question, so the 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 clearly identifies a specific behavior: a hallucination-resistant answer mode that extracts answers only from tool results, with the same routing as ask_pipeworx. It differentiates itself from its sibling by emphasizing grounded extraction and explicit refusal behavior rather than unconstrained generation.

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?

The description explicitly states when to use this tool: 'whenever an answer will be quoted, cited, or acted on' and when not to use it: 'prefer ask_pipeworx for casual lookups.' It also references the sibling tool and the cost trade-off, giving clear selection guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Uk Food Hygiene toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Several tools blur together: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer questions over the same data, and five polymarket_* tools overlap on edge detection and arbitrage. Descriptions help somewhat, but the boundaries are subtle and the server name 'Uk Food Hygiene' adds a layer of confusion.

Naming Consistency3/5

All names are lowercase snake_case, but conventions vary widely: brand-style names (ask_pipeworx, pipeworx_feedback), noun phrases (entity_profile, recent_changes), verb_noun pairs (list_subscriptions, validate_claim), and bare verbs (recall, forget). It is readable but does not follow one predictable pattern.

Tool Count1/5

A server named 'Uk Food Hygiene' has 33 tools, of which only two (uk_food_hygiene_search, uk_food_hygiene_details) relate to food hygiene. The rest are a grab-bag of Pipeworx platform utilities, prediction-market tools, memory helpers, and subscription features — an extreme mismatch between count and stated scope.

Completeness2/5

The two food hygiene tools cover search and detail lookup, which handles the core use case, but the broader tool surface has notable gaps: citation URIs are returned but no fetch/read tool exists, and the unrelated domains (prediction markets, company research, AI visibility) are deep in some places and absent in others. The overall surface feels like an incoherent collection rather than a complete domain toolkit.

Resources