Skip to main content
Glama

Uk Ons

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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / value / description
      Previous value: -"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\")."New value: +"For 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."
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already establish readOnly/openWorld/idempotent/non-destructive behavior, and the description adds substantial behavioral context beyond that: internal cascade across multiple lookup endpoints, graceful degradation when GLEIF/OpenFIGI are unavailable, explicit unresolved reporting, ambiguity handling via figi_candidates, and the ISIN-to-LEI mapping behavior. No contradiction with annotations.

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 long but dense and front-loaded with examples and the key 'Use FIRST' directive. Each subsection earns its place by clarifying supported types, edge cases, and behavior. It could be trimmed slightly, but the structure is logical and scannable.

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 well: it names figi_candidates, unresolved, source labels, RxCUI/ingredient/brand, and enrichment degradation. Given the tool's complexity and breadth of entity types, the description is complete enough for an agent to select and invoke it correctly.

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?

Although the schema covers 100% of parameters, the description adds significant semantics: examples of ticker/CIK/ISIN/name inputs, the distinction between brand and generic drug names, and the crucial guidance that bond lookups should receive the issuer name exactly as printed, not the full noun phrase. 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 precise verb and resource: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It distinguishes itself from sibling tools like entity_profile by scoping itself to identifier lookup rather than broader profiling, and it enumerates specific supported types and example queries.

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 gives explicit usage guidance: 'Use FIRST whenever you have a name but need an ID.' It also explains when identifiers may not resolve, when ambiguity is handled via figi_candidates, and how enrichment degrades gracefully. It does not explicitly name sibling tools as alternatives or state when not to use it, but the direction is clear and actionable.

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

All Uk Ons toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation3/5

Most tools have clearly separated jobs, but the set is crowded with overlapping research/query entry points: ask_pipeworx and ask_pipeworx_beta are described as currently identical, and the Polymarket/research cluster has fuzzy boundaries. Detailed descriptions help, but an agent can still easily mis-select among these tools.

Naming Consistency3/5

Names are consistently snake_case and readable, but they mix verb-led commands (get_dataset, list_editions, validate_claim) with noun-led descriptive names (entity_profile, polymarket_edges, recent_changes) and a version-suffixed duplicate (ask_pipeworx_beta). The ONS tools also lack a shared ons_ prefix aside from ons_timeseries, so the naming is more a collection of conventions than one predictable pattern.

Tool Count2/5

37 tools is well past the heavy range for a server whose name suggests a focused UK ONS statistics surface; only about six tools actually serve ONS data, while the rest span memory, subscriptions, prediction markets, npm scanning, AI visibility, and general Pipeworx plumbing. The count is not an extreme 50+ sprawl, but it is too many for a coherent scope.

Completeness4/5

The core ONS read workflow is well covered: catalog discovery through list_datasets, dataset/edition/version metadata, filtered get_observations, and classic time series via ons_timeseries. Minor gaps exist, such as no dedicated dataset search and some peripheral one-off features like scan_dependency or generate_llms_txt, but agents can work around them.

Resources