Skip to main content
Glama

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

A4.3/5.0
Behavior5/5

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

Despite annotations already marking readOnly/idempotent, the description adds rich behavioral detail: exact identifiers returned per type (CIK/ticker/company_name, LEI, FIGI, RxCUI/ingredient/brand + citation), an explicit `unresolved` field rather than omission, `figi_candidates` for ambiguous names, and graceful degradation when GLEIF/OpenFIGI are unavailable. This goes well beyond the annotation signals.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

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

The description is densely packed but poorly organized, with numerous run-on clauses. The segment 'more than one instrument it asserts nothing and returns `figi_candidates` to pick from' is grammatically broken and confusing, undermining readability. While front-loaded with the purpose, the middle section should have been restructured or split into clear sentences.

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?

For a two-type resolver with no output schema, the description covers input forms, outputs, ambiguity handling, and failure behavior, including a fallback if external sources are offline. The only hiccup is that the garbled figi_candidates sentence slightly blurs the explanation of ambiguous results, so completeness is strong but not perfect.

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 baseline is 3; the description adds value by disclosing that ISIN is also accepted as company input and by explaining ISIN-to-LEI mapping for non-US issuers. It also restates the entity-name-only rule, reinforcing the schema's warning about noun phrases.

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 states a specific verb+resource: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It distinguishes itself as the ID-resolution first step, which separates it from siblings like entity_profile and compare_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 gives an explicit when-to-use: 'Use FIRST whenever you have a name but need an ID.' This is clear context, though it stops short of naming sibling tools or saying when NOT to use it (e.g., if an ID is already available). Still, the examples and 'FIRST' make the intended usage unambiguous.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.7/5.0
Disambiguation2/5

Several tools have significantly overlapping purposes, especially the ask_pipeworx family, deep_research, and validate_claim, plus a dense cluster of polymarket_* tools and two AI-visibility checkers. The ship-related tools are distinct, but an agent would struggle to choose among the many broadly similar query/research tools.

Naming Consistency3/5

Names are mostly snake_case and readable, but they follow no consistent convention: generic one-word verbs like remember and forget sit alongside branded names like ask_pipeworx, noun-style names like entity_profile, and prefix families like polymarket_*. The inconsistency is noticeable but not chaotic.

Tool Count2/5

34 tools is well above the well-scoped range, and the vast majority are unrelated to the server name 'Vessel Tracking'. The live-ship tools are a tiny minority buried inside a broad general-purpose data, research, and prediction-market platform, making the overall set feel bloated and misaligned with its stated identity.

Completeness1/5

As a vessel-tracking server, the surface is severely incomplete: only ais_coverage_check, live_ship_position, and live_ships_in_area relate to shipping, with no vessel lookup by name/IMO, no historical positions, no voyage data, and no port-call information. The actual tool set is rich as a general data platform, but that is not what the server name promises.