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Trove

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

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds substantial context beyond these: it explains the internal cascade of lookup endpoints, the graceful degradation when GLEIF/OpenFIGI are unavailable, the explicit handling of unresolved identifiers under `unresolved`, and the fact that when multiple matches exist it returns `figi_candidates` rather than asserting. This fully discloses behavioral nuances without contradicting any 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 every sentence contributes necessary detail—examples, sources, behavioral notes, and formatting caveats. It is front-loaded with the 'Use FIRST' directive and organized with a clear 'SUPPORTED TYPES' section. While it could be tighter (some redundancy in explaining cascade and graceful degradation), it remains efficient given the tool's complexity.

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?

For a tool with only two parameters and no output schema, the description is remarkably complete. It explains what identifiers are returned for each type, how sources are labelled, how unresolved identifiers are surfaced, how ambiguity is handled (candidates), and how failures degrade. An agent has everything needed to call this tool correctly and interpret the result, despite no output schema.

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?

The schema already covers both parameters with 100% coverage, but the description adds crucial semantics. For `value`, it warns against passing full noun phrases and gives exact formatting rules ('for a bond that is the ISSUER exactly as printed'). For `type`, it explains the meaning of each enum value and ties them to the returned identifiers. This goes well beyond the schema's basic descriptions and eliminates ambiguity.

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 is exceptionally clear about the tool's purpose: resolving a user-spoken name to canonical identifiers. It lists concrete example queries, names the identifier types (CIK, LEI, RxCUI, FIGI), and explicitly contrasts with manual lookups ('replaces 2-3 manual lookups'). It also differentiates from sibling tools by its specificity—this is the go-to tool when you have a name but need an ID.

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 opens with 'Use FIRST whenever you have a name but need an ID,' providing an explicit trigger condition. It also details supported types ('company', 'drug'), accepted input formats (ticker, CIK, ISIN, name), and edge-case guidance (e.g., for bonds, pass the issuer exactly as printed, not the full noun phrase). This gives the agent clear, actionable instructions on when and how to invoke the tool.

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

A3.7/5.0
Disambiguation3/5

Several tools have overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_grounded, deep_research all answer questions; entity_profile, compare_entities, recent_changes all cover company data). Descriptions provide distinctions, but an agent can easily misselect, especially between the Pipeworx query tools.

Naming Consistency2/5

Tool names follow mixed conventions: some use verb_noun (list_subscriptions, unsubscribe), others use descriptive phrases (ai_visibility_check, polymarket_arbitrage) or nouns (deep_research, entity_profile). No consistent pattern, making it harder to predict tool names.

Tool Count2/5

With 32 tools covering diverse domains (data lookups, prediction markets, memory, subscriptions), the server feels overloaded. The scope would be better served by splitting into smaller, focused servers (e.g., data query, prediction market, memory). Many tools are peripheral to a core purpose.

Completeness4/5

The tool surface is fairly comprehensive within its domains: CRUD for memory (remember/recall/forget), subscription management, extensive data query options, and prediction market analysis. Minor gaps exist (no update memory, no direct trading), but agents can work around them.

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