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Glama

Latam Validate

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?

The description goes well beyond the annotations, disclosing internal cascading lookups, graceful degradation when GLEIF/OpenFIGI are unavailable, explicit reporting of unresolved identifiers, and the 'figi_candidates' behavior for ambiguous matches. This rich behavioral detail complements the readOnly/openWorld/idempotent hints without contradicting them.

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-structured, front-loaded with user-intent examples and organized around supported types and key behaviors. While every sentence mostly earns its place, some details could be tightened without losing meaning; the density is high but not wasteful.

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 complex tool with no output schema, the description is remarkably complete. It covers input handling, supported entities, return identifiers (CIK, ticker, LEI, FIGI, RxCUI), ambiguity behavior, unresolved identifiers, degradation, and internal multi-step execution. An agent has enough context to invoke the tool correctly and interpret its results.

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 semantics beyond the schema: it explains what each enum type resolves to, gives input format guidance for 'value', warns against including full noun phrases, and even explains ISIN-to-LEI resolution. This is exemplary parameter-level documentation.

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 clearly states the tool's verb ('resolve'), its resource ('user-spoken NAME to canonical/official identifiers'), and its role as the first stop when an ID is needed. It differentiates from sibling tools like entity_profile and compare_entities by explicitly framing itself as the resolver for identifiers other tools require as input.

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 explicit usage guidance: 'Use FIRST whenever you have a name but need an ID', supported by concrete user-phrase examples. It also explains the tool's scope across company and drug types, and describes tricky cases like bond issuer names. It does not explicitly list exclusions or alternatives, but the context is strong enough to guide selection.

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

A3.6/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route the same 5,702 tools with only subtle differences, and ai_visibility_check is essentially a single-entity subset of scan_competitor_ai_presence. The memory trio (remember/recall/forget) and subscription tools also sit awkwardly alongside the data-query tools, making selection genuinely ambiguous.

Naming Consistency2/5

Naming mixes verb_noun (validate_cnpj, resolve_entity, compare_entities), noun_verb (bet_research, entity_profile, recent_changes), and bare nouns with inconsistent suffixes (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk). The ask_pipeworx family uses inconsistent qualifiers (beta vs grounded), and validate_* is used for both checksum-only tools and full lookups.

Tool Count2/5

36 tools is excessive for a server ostensibly named 'Latam Validate' — only 5 tools relate to LATAM validation while the rest form a sprawling general-purpose data and prediction-market platform. Many tools could be consolidated (the ask_pipeworx family, the polymarket_* family, the memory trio), suggesting the set is over-scoped for any single agent's typical workflow.

Completeness2/5

For the stated LATAM validation purpose, coverage is thin: only Brazil (CPF/CNPJ/CEP/banks) and Mexico (CLABE) are covered, with no validators for other LATAM jurisdictions. For the broader Pipeworx data platform, the surface is extensive but has notable gaps — grounded retrieval, claim verification, and arbitrage tools exist, yet many LatAm-specific data sources and common validation formats (RFC, RUT, DNI, CURP) are absent.

Resources