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Cms

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 goes well beyond the readOnly/idempotent/openWorld annotations by disclosing ambiguous-match behavior (returns figi_candidates rather than asserting), explicit unresolved fields, source labels on each identifier, and graceful degradation when GLEIF/OpenFIGI are unavailable. These are genuine behavioral facts an agent could not infer from annotations alone.

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 content is front-loaded (trigger phrases and 'Use FIRST' come early) and nearly every clause carries decision-relevant detail. However, the company section is one massive nested-parenthetical paragraph with long run-on lists, which makes scanning harder than it needs to be; some bulletization would improve it.

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 full burden of return semantics, and it does: it lists resolved identifiers per source, unresolved handling, figi_candidates on ambiguity, partial-failure behavior, and drug citation format. For a two-type, multi-backend resolver, nothing essential 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 with 100% schema coverage, the description adds significant value: it warns to pass the ENTITY NAME ONLY, gives a concrete counterexample ('NEW YORK ST DORM AUTH' rather than 'NEW YORK ST DORM AUTH revenue bonds'), and explains why (FIGI matches instrument names). It also clarifies which value forms are valid per type beyond the schema's short enum/label.

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 opens with concrete trigger queries and states the core action: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' SUPPORTED TYPES then enumerate exactly what is returned for company vs drug, making the purpose and scope unmistakable and distinct from sibling query/profile tools.

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?

It gives explicit selection guidance ('Use FIRST whenever you have a name but need an ID') and clarifies broad accepted inputs (ticker, CIK, ISIN, brand/generic name), plus how it handles 'who owns X'/'is X a subsidiary of Y'. It never names sibling alternatives such as entity_profile or compare_entities or states when not to use them, so it stops short of full exclusion guidance.

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

A4/5.0
Disambiguation3/5

Some tools have clearly distinct purposes (remember/recall/forget, subscribe/unsubscribe), but the ask_pipeworx family overlaps heavily — ask_pipeworx_beta is explicitly identical today, and ask_pipeworx_grounded/deep_research are variations on the same routing core. Polymarket tools and comparison/profile tools also have fuzzy boundaries, though detailed descriptions help agents choose.

Naming Consistency4/5

Most tools follow a lowercase snake_case verb_noun pattern (search_datasets, get_dataset, validate_claim, resolve_entity). A few deviate with bare verbs (remember, forget, recall) or noun-like names (dataset_info, entity_profile, pipeworx_trending), but the overall style is predictable and readable.

Tool Count2/5

34 tools is a heavy surface for one server, and the scope sprawls across CMS open data, general data research, prediction markets, memory storage, and subscription management. Many of these could be split into separate coherent servers, and several meta-routers (ask_pipeworx, deep_research, discover_tools, suggest_questions) overlap in purpose.

Completeness4/5

Within the broad data-research domain the set is fairly complete: search, retrieval, grounding, comparison, entity resolution, verification, subscriptions, and memory are all covered with no obvious dead ends. However, the server is named 'Cms' yet only three tools actually touch CMS datasets, leaving that narrow purpose under-covered relative to the rest.

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