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Glama

Cnpj Br

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

A4.6/5.0
Behavior5/5

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

Discloses several behaviors beyond the readOnly/idempotent annotations: the internal cascade ('replaces 2-3 manual lookups'), graceful degradation ('if GLEIF or OpenFIGI is unavailable, the EDGAR identifiers still return'), and explicit unresolved-handling ('stated explicitly under `unresolved` rather than omitted'). 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.

Conciseness4/5

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

Front-loaded with realistic query examples and a crisp purpose statement, followed by labeled sections for supported types and degradation. The description is long but every clause carries operational detail; minor verbosity in the parentheticals prevents a perfect score.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Complete for input semantics, supported types, failure behavior, and return expectations given no output schema. It could briefly mention ambiguity handling (duplicate names) or the exact response envelope, but the current detail is sufficient for a 2-parameter resolver with strong annotations.

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?

Adds substantial meaning beyond the schema. For type=company, it enumerates the cross-source identity spine (CIK, ticker, LEI, FIGI) and the exact-ticker-map vs name-search behavior; for type=drug it details RxCUI/ingredient/brand. The value description goes further with formatting examples and a warning against trailing security-class words.

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?

States a specific verb ('resolve') and resource ('user-spoken NAME' to canonical identifiers), with concrete example phrasings and supported types. It clearly positions the tool as the first step when an ID is needed, which distinguishes it from sibling lookup tools like cnpj_lookup or entity_profile.

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?

Gives an explicit directive: 'Use FIRST whenever you have a name but need an ID', and lists example user phrasings that should trigger this tool. It does not explicitly name sibling alternatives or state when not to use it, but the 'first step' guidance supplies strong contextual 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

A3.5/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical variants; entity_profile, compare_entities, recent_changes, and deep_research have overlapping research scopes; and the six Polymarket tools cover similar ground. Individual descriptions are detailed, but an agent could easily select the wrong meta-tool.

Naming Consistency3/5

Nearly all names use snake_case, which is readable, but the pattern is inconsistent: some are verb_noun (resolve_entity, list_subscriptions), some are bare noun phrases (entity_profile, polymarket_edges, recent_changes), and memory tools are single verbs (remember, recall, forget). There is no predictable naming convention across the set.

Tool Count2/5

32 tools is already at the heavy end, but the real problem is scope mismatch: a server named 'Cnpj Br' dedicates 31 of 32 tools to Pipeworx data research, prediction markets, memory, and web utilities, with only a single CNPJ lookup. The count feels bloated and incoherent for the apparent purpose.

Completeness1/5

For the stated CNPJ/Brazil domain, the entire surface is one lookup tool: no search by company name, no CNAE/industry breakdowns, no batch or comparative lookups, and no related Brazil KYB data. As a CNPJ-focused server it is severely incomplete.

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