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Glama

Brreg No

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?

The description goes well beyond the annotations by explaining cascade behavior, graceful degradation when GLEIF/OpenFIGI are unavailable, explicit `unresolved` reporting, `figi_candidates` handling, and source-labelled identifiers. These behavioral details are not present in the annotations and are highly useful for anticipating tool output.

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 it is well-structured with examples, a clear 'SUPPORTED TYPES' split, and front-loaded key instructions. Some repetition of schema details exists, but the density of practical guidance justifies the length.

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 having no output schema, the description covers return behavior, edge cases, enrichment fallbacks, and what the agent should expect when matches are ambiguous. It provides enough context for an agent to call the tool correctly and interpret initial results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds meaningful semantic guidance: accepted input forms, the difference between ticker/CIK/ISIN/name for companies, and an explicit warning to pass only the entity name rather than the full noun phrase. This exceeds the schema's own descriptions.

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 states a specific purpose with clear verbs: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It further defines supported entity types and contrasts itself with other tools by saying 'Use FIRST whenever you have a name but need an ID.' This makes it easy to distinguish from siblings like get_entity or search_entities.

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?

It explicitly instructs 'Use FIRST whenever you have a name but need an ID,' which is a strong usage signal. It also clarifies scope by listing supported types and edge cases, though it does not name specific alternative tools or explain when not to use resolve_entity beyond the implied 'name-to-ID' scenario.

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
Disambiguation1/5

ask_pipeworx_beta is explicitly identical to ask_pipeworx, and multiple other tools overlap heavily: ai_visibility_check/scan_competitor_ai_presence, discover_tools/suggest_questions, and polymarket_arbitrage/polymarket_edges/polymarket_edge_tracker all sit in nearly the same functional space. An agent would struggle to reliably select the right tool among these clusters.

Naming Consistency3/5

Tool names are consistently snake_case and mostly readable, but the pattern is mixed: get_/search_ verbs coexist with product-prefixed names (pipeworx_*, polymarket_*), noun-style names (entity_profile, recent_changes), and bare verbs (remember, forget). The conventions are not chaotic, but they are not predictable enough for a coherent set.

Tool Count2/5

37 tools is far too many for a server named 'Brreg No,' which should be a focused Norwegian business-registry lookup server. Only a handful of tools actually target Brreg (search_entities, get_entity, get_accounts, get_roles, get_sub_entity, search_sub_entities); the rest are unrelated Pipeworx/Polymarket/meta tools that drown out the core purpose.

Completeness3/5

The core Brreg read surface is present: entity search/lookup, sub-entities, financial accounts, and roles. However, there is no Brreg change/update feed or document-level coverage, and the unrelated generic research tools do not fill that gap. For a registry-focused server, the coverage is workable but not complete.

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