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Overpass

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?

Annotations already mark the tool read-only and idempotent, and the description adds significant context: cascading internal lookups, graceful degradation when GLEIF/OpenFIGI are unavailable, candidate-only behavior on ambiguous matches, source-labelled identifiers, and explicit unresolved fields. 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 information-dense but overlong, with long parentheticals and repeated guidance. It is front-loaded with purpose, but the formatting could be tightened without losing the valuable edge-case semantics.

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?

Despite lacking an output schema, it describes return behavior for both supported types, candidate ambiguity handling, unresolved identifiers, and fallback behavior. Coverage of the tool's complexity is strong for a two-parameter read-only lookup.

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?

With 100% schema coverage, the description still adds important meaning: examples for ticker/CIK/name, ISIN-to-LEI behavior, RxNorm citation format, and the warning to pass issuer names exactly as printed without security-class trailing words. 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 opens with concrete user phrasings and an explicit verb: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It distinguishes itself from ID-consuming siblings by stating the input/output relationship and listing supported entity types.

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?

'Use FIRST whenever you have a name but need an ID' is an explicit trigger condition. It does not name or exclude direct sibling tools, but the condition is clear enough to route an agent for the common lookup case.

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

A3.8/5.0
Disambiguation3/5

Most tools have distinct purposes, but the ask_pipeworx trio (stable, beta, grounded) are near-identical variants, and the five polymarket_* tools plus bet_research heavily overlap in the edge-finding space. Detailed descriptions help, but an agent could easily misselect among these clusters.

Naming Consistency3/5

Names are all snake_case and readable, but conventions vary: ask_pipeworx_* uses a prefix pattern, polymarket_* is consistent, yet others mix verbs (scan_competitor_ai_presence, generate_llms_txt) with nouns (entity_profile, resolve_entity). No single verb_noun pattern governs the set.

Tool Count2/5

34 tools is well above the 25-tool threshold and feels like multiple servers merged into one: structured data routing, prediction markets, OSM, memory, subscriptions, and AI-visibility checks. The breadth is impressive but the count is heavy for a single tool surface.

Completeness4/5

The surface is notably complete for its blended domain: memory has remember/recall/forget, subscriptions have subscribe/list/unsubscribe, data access has multiple router modes plus deep research and claim validation, and prediction markets have research, edge, arbitrage, fill-risk, and cross-venue tools. Minor gaps exist (no subscription update, no explicit reverse-geocoding tool), but core workflows are covered.

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