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Color Pizza

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

A4.6/5.0
Behavior5/5

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

Beyond the readOnlyHint and idempotentHint annotations, the description discloses several important behaviors: internal cascading lookups, graceful degradation when GLEIF/OpenFIGI are unavailable, source-labelled identifiers, and explicitly listing unresolved identifiers under an 'unresolved' field. 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.

Conciseness3/5

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

The description is front-loaded with user questions and a clear imperative, but the 'company' type is written as one long nested parenthetical with run-on semicolons. It is information-dense and valuable, but hard to scan and could benefit from bullets or clearer paragraph separation.

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 compensates by enumerating returned fields for both company and drug types, naming the unresolved behavior, and covering edge cases such as non-US ISIN resolution and non-equity instruments. The agent has enough context to call 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?

The input schema describes the two params, but the description adds essential semantics: accepted value forms (ticker, CIK, ISIN, name; brand or generic drug name), the ISIN-to-LEI behavior, and the critical 'ENTITY NAME ONLY' warning with bond examples. This goes well beyond what the schema provides.

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 realistic user queries and then states a specific, differentiated purpose: resolving a spoken name to canonical identifiers that other tools require. It positions the tool as the first step in a lookup workflow, making it clear it is not a generic search or profile tool.

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' and provides supported types, input formats, and edge cases such as ISIN and non-equity instruments. However, it does not explicitly name alternative sibling tools or state when not to use this tool, 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

A4.3/5.0
Disambiguation4/5

Most tools have distinct purposes, such as `entity_profile` for company profiles and `recent_changes` for updates. However, some overlap exists between `ask_pipeworx` and `ask_pipeworx_grounded` (both query data, one with hallucination resistance), and among Polymarket-related tools (`bet_research`, `polymarket_edges`, `polymarket_arbitrage`). Detailed descriptions help distinguish them.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern (e.g., `ai_visibility_check`, `ask_pipeworx`, `bet_research`, `compare_entities`). No mixing of camelCase or other conventions. The names are descriptive and predictable.

Tool Count3/5

At 29 tools, the server is on the heavy side. While each tool serves a specific function, the broad scope (color naming, Polymarket betting, company research, etc.) makes the count feel slightly high for a single server, though still manageable.

Completeness4/5

The tool set covers color naming, company research, Polymarket, AI visibility, memory, subscriptions, and data queries comprehensively. Minor gaps exist (e.g., no direct SEC filing retrieval without compound tools), but overall it provides a well-rounded surface for its diverse domain.

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