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Uk Food Hygiene

by io.github.pipeworx-io

Resolve Entity

resolve_entity
Read-onlyIdempotent

"What's the ticker for…" / "find the CIK for…" / "what's the LEI for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" / "who owns X" / "is X a subsidiary of Y" — resolve a user-spoken NAME to the canonical/official identifiers other tools require as input. Use FIRST whenever you have a name but need an ID. SUPPORTED TYPES: "company" (cross-source identity spine: 10-digit CIK + ticker + company_name from SEC EDGAR, legal-entity LEI from GLEIF with parent/ultimate-parent/children ownership when the LEI resolves, and security FIGI from OpenFIGI — by exact ticker map when a ticker is implied, and otherwise by name search, so NON-EQUITY instruments that never have a ticker (municipal and corporate bonds, notes, authority debt) DO resolve here; when a name matches more than one instrument it asserts nothing and returns figi_candidates to pick from, which is the correct answer to an issuer name that does not identify a single bond; every identifier is labelled with the source that established it, and an identifier that could NOT be resolved is stated explicitly under unresolved rather than omitted — accepts ticker, CIK, ISIN, or company name as input; an ISIN like "CH0038863350" resolves to the LEGAL ENTITY that issued the security via the GLEIF ISIN-to-LEI mapping, covering non-US issuers EDGAR cannot reach), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/concept/{rxcui} citation; accepts brand or generic name). LEI/FIGI enrichment degrades gracefully — if GLEIF or OpenFIGI is unavailable, the EDGAR identifiers still return. Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type: "company" or "drug".
valueYesFor company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., "ozempic", "metformin"). Pass the ENTITY NAME ONLY — for a bond that is the ISSUER exactly as printed ("NEW YORK ST DORM AUTH"), never the question's full noun phrase ("NEW YORK ST DORM AUTH revenue bonds"): the FIGI lookup matches instrument names, so trailing security-class words match nothing.

TDQS

A4.7/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds substantial behavioral context beyond the read-only/idempotent annotations: graceful degradation if GLEIF or OpenFIGI is unavailable, cascading across multiple internal lookup endpoints, explicit unresolved identifiers instead of omission, and coverage of non-equity instruments. This is rich, useful disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with natural-language examples and organized into supported types, but it is lengthy and contains dense parenthetical run-ons. Still, almost every clause carries meaningful semantic weight for a tool this complex.

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 carries the burden of explaining return behavior, and it does so thoroughly: for company it lists CIK, ticker, company_name, LEI ownership data, and FIGI; for drug it lists RxCUI, ingredient, brand, and citation; it also explains fallback and unresolved behavior. Nothing essential for invoking the tool is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Even though schema coverage is 100%, the description adds critical parameter nuance not in the schema: pass only the entity name, use the issuer's exact printed name for bonds, avoid trailing security-class words, and what value types are accepted for company vs. drug. This materially improves correct invocation.

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 verb and resource: resolve a user-spoken NAME to canonical/official identifiers required by other tools. It gives concrete example queries, names supported types, and makes the tool's role distinct from sibling tools like entity_profile and compare_entities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Use FIRST whenever you have a name but need an ID,' which is a clear usage directive. It also notes that one call replaces 2-3 manual lookups, but it does not name specific alternatives or state explicit when-not-to-use conditions.

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

A3.6/5.0
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