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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?

Beyond the readOnly/idempotent annotations, the description discloses internal cascading lookups, graceful degradation when GLEIF/OpenFIGI are unavailable, a figi_candidates fallback when multiple instruments match, an explicit 'unresolved' field rather than silent omission, and a pipeworx citation for drug lookups. This is rich behavioral context that annotations alone do not provide.

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 and dense, but nearly every sentence earns its place given the tool's complexity and dual entity types. The opening examples are somewhat repetitive, yet they quickly convey intent. The structure moves from purpose to supported types to degradation behavior, which is logical.

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: returned identifiers, figi_candidates, unresolved results, RxCUI with citation, and graceful degradation. It also covers all input modes and edge cases like non-US issuers and non-equity instruments. This is complete enough for an agent to select and invoke the tool 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?

Schema coverage is 100%, but the description adds substantial practical meaning: accepted value formats (ticker, CIK, ISIN, company name; brand or generic drug name), concrete examples, and an important caveat to pass the entity name only and not the full noun phrase for bonds. This goes well beyond the schema's generic property descriptions.

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 user queries then states a precise purpose: resolve a user-spoken NAME to canonical/official identifiers other tools require as input. It also lists supported entity types (company, drug) and the specific identifier systems used (SEC EDGAR, GLEIF, OpenFIGI, RxNorm), making it distinct from siblings like entity_profile or 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?

It explicitly says 'Use FIRST whenever you have a name but need an ID,' which gives a clear trigger condition. It does not explicitly contrast with alternatives such as entity_profile or compare_entities, but the resolver-vs-profile distinction is strongly implied by the identifier-focused wording and examples.

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
Disambiguation4/5

Tool purposes are mostly distinct, with detailed descriptions differentiating similar-sounding tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research. However, some overlap exists between bet_research and polymarket_* tools, requiring careful reading of descriptions to select the correct one.

Naming Consistency3/5

Tool names mix verb_noun patterns (e.g., resolve_entity, validate_claim) with noun_phrases (e.g., entity_profile, recent_alerts) and occasional inconsistencies like pipeworx_trending or search_within. While most names are readable, the lack of a uniform convention makes it harder to predict tool names.

Tool Count4/5

32 tools cover a broad domain of data retrieval, analysis, prediction markets, and system management. The count is high but reasonable given the extensive feature set, though a more focused set could improve coherence.

Completeness4/5

The tool set covers major needs: data lookup, comparison, validation, monitoring, and memory. Missing CRUD operations for external data are expected as this is a read-centric API, so gaps are minor and don't hinder common tasks.

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