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

A4.7/5.0
Behavior5/5

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

Even with readOnly/idempotent annotations, the description goes well beyond them: it explains multi-match ambiguity (returns figi_candidates, asserts nothing), explicit unresolved listing, graceful degradation when GLEIF/OpenFIGI is unavailable, and ISIN-to-legal-entity mapping. It also discloses the internal cascade of lookups and that enrichment is best-effort.

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 dense but front-loaded with purpose and trigger phrases, then organized by supported type. Some parenthetical clauses are lengthy, but nearly every sentence adds behavioral or routing value; the slight over-length keeps it from a perfect 5.

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?

With no output schema, the description carries the full burden of explaining return semantics and does so thoroughly: identifiers per type, source labelling, unresolved handling, figi_candidates, degradation, and input formats. Nothing essential is missing for an agent to call this tool correctly.

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%, so the baseline is 3, but the description substantially enriches both parameters: it explains what each entity type resolves to and, for value, gives exact formatting rules (entity name only, bond issuer as printed, never trailing security-class words). This goes well beyond the schema 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 opens with concrete user-phrase triggers and a clear verb+resource: resolving a spoken name to canonical/official identifiers. It defines the two supported types and explicitly positions itself as the input provider for other tools, separating it from siblings like entity_profile or compare_entities even without naming them.

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 explicit guidance: 'Use FIRST whenever you have a name but need an ID,' plus trigger phrases and supported type details. It does not, however, name specific alternatives or state when not to use it, so it stops short of full when/when-not routing.

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

A3.6/5.0
Disambiguation2/5

Several tool clusters have genuinely blurry boundaries: ask_pipeworx, ask_pipeworx_beta (explicitly 'currently matches ask_pipeworx exactly'), ask_pipeworx_grounded, and deep_research all route questions to the same 5,743-tool catalog, and the six Polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) heavily overlap on 'should I bet / where is the edge'. The descriptions are detailed and cross-reference each other, but an agent would still struggle to pick correctly among near-duplicates.

Naming Consistency3/5

Everything is uniformly snake_case and readable, but there is no consistent verb_noun pattern: verb_noun (resolve_entity, discover_tools, validate_claim) mixes with noun_noun (domain_search, entity_profile, polymarket_arbitrage), adjective_noun (deep_research, recent_changes), bare verbs (forget, recall, remember), and brand prefixes (pipeworx_*, ask_pipeworx_*). Readable, but patternless.

Tool Count2/5

34 tools is well above the heavy threshold, but the bigger issue is scope incoherence: a server named 'Hunter' dedicates only 3 of 34 tools to Hunter.io email lookup while the remaining 31 belong to an unrelated Pipeworx data-research/prediction-market platform. The count is not earned by a single coherent purpose.

Completeness3/5

The Pipeworx research core is quite thorough: entity resolution, single-lookup, grounded answers, deep research, profiles, comparisons, claim validation, semantic search-within-records, change feeds, subscriptions, and memory form a full research lifecycle. However, the domain is a grab-bag spanning email finding, npm dependency checking, prediction markets, and data research, and the three Hunter.io tools that match the server name are only a thin fragment of the surface.