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Madrid Events

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

A4.5/5.0
Behavior5/5

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

Beyond the annotations, the description discloses important behavior: it cascades through lookup endpoints, degrades gracefully when GLEIF/OpenFIGI are unavailable, returns figi_candidates on ambiguous matches, labels identifier sources, and explicitly lists unresolved identifiers. This is rich, non-obvious behavioral context that materially helps an agent predict outcomes.

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 well organized: usage rule, supported types, caveats, and degradation behavior. It is front-loaded with example queries and the 'Use FIRST' directive. The company-type parenthetical is dense and could be trimmed, so it is not maximally concise, but every section earns its place.

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 no output schema, the description provides a remarkably complete picture: input types, output identifier families, behavior on ambiguity, source attribution, partial failure handling, and even a citation pattern. An agent has enough context to invoke it correctly and interpret unusual outcomes.

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?

Although the schema already documents both parameters and has 100% coverage, the description adds substantial semantic guidance: concrete examples for type, acceptable input forms for value, the instruction to pass the entity name only, and a warning about trailing security-class words breaking FIGI lookup. This goes well beyond a baseline 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb (resolve) and resource (a user-spoken name to canonical/official identifiers), listing supported types and examples. It does not explicitly distinguish this tool from sibling tools like entity_profile or search_within, though the intended role is clear.

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 gives a clear directive: 'Use FIRST whenever you have a name but need an ID.' It also explains when resolution returns candidates rather than asserting a single result. It does not explicitly discuss when not to use the tool or name sibling alternatives, so it falls just short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Madrid Events toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

The set contains several heavily overlapping clusters: three ask_pipeworx variants (with ask_pipeworx_beta explicitly identical to ask_pipeworx right now) and six polymarket-related tools that all orbit edge detection, arbitrage, and fill risk. An agent choosing among bet_research, polymarket_edges, polymarket_arbitrage, and polymarket_kalshi_spread would have a hard time picking the right one.

Naming Consistency3/5

Most tools use readable snake_case, so the naming is not chaotic. However, the pattern is mixed: some are verb_noun (compare_entities, resolve_entity), some are brand-style (ask_pipeworx, ask_pipeworx_beta), and some are noun-only phrases (events, polymarket_arbitrage, pipeworx_trending). It is consistent in casing but not in structural convention.

Tool Count2/5

32 tools is well beyond the usual well-scoped range, and the count is inflated by multiple near-duplicate clusters for querying, prediction markets, and memory/subscription utilities. For a server named Madrid Events, this is especially disproportionate since only one tool actually relates to Madrid events.

Completeness2/5

Relative to the Madrid Events name, the domain coverage is almost entirely missing: only events addresses the stated purpose, and it is read-only with no detail view, booking, or management operations. If interpreted as the broader Pipeworx platform, coverage is richer, but the server's stated identity makes the gap severe.

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