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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. Added

TDQS

A4.4/5.0
Behavior5/5

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

Despite annotations declaring readOnlyHint, openWorldHint, and idempotentHint, the description adds substantial behavioral context: it cascades through multiple endpoints internally, asserts nothing when multiple FIGI candidates exist, explicitly reports unresolved identifiers, degrades gracefully when external sources fail, and labels identifiers with their provenance. This goes far beyond what the annotations capture.

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 an extremely long and dense wall of text with no bullet points, headings, or clear hierarchical structure. While every sentence carries information, the lack of formatting and the sheer volume make it difficult to parse quickly. It is under-structured rather than concise, earning a low score on this dimension.

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 two entity types, multiple identifier sources, and fallback behaviors, the description covers all necessary runtime aspects: what is returned for each type, how to handle ambiguous matches, behavior on source failures, and the internal cascading mechanism. There is no output schema, so the description correctly takes responsibility for explaining return values and edge cases. Nothing critical is missing.

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?

The schema already covers both parameters at 100%, including format guidance for 'value'. The description supplements this by noting that 'value' can also accept an ISIN for company type (not mentioned in the schema) and clarifies the exact naming convention for bonds ('never the question's full noun phrase'), adding meaning beyond the schema. This justifies a rating above the baseline of 3.

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 verb ('resolve') and resource ('a user-spoken NAME to the canonical/official identifiers') and clearly distinguishes itself from sibling tools by positioning as a prerequisite for tools that require IDs. It provides multiple example queries that map directly to this tool, making its purpose unambiguous to an agent.

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 explicitly says 'Use FIRST whenever you have a name but need an ID' and gives numerous examples of when this applies, including detailed handling for companies, drugs, and bonds. However, it does not explicitly name alternative tools or state when NOT to use it (e.g., when an ID is already known). This is a minor omission given the clarity of the primary condition.

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 are near-duplicates (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded), and there are overlapping clusters among the polymarket_* tools, research tools (deep_research, entity_profile, compare_entities, recent_changes), and AI-visibility tools (ai_visibility_check vs scan_competitor_ai_presence). The detailed descriptions help, but an agent selecting among these could easily pick the wrong one.

Naming Consistency2/5

Snake_case is used consistently, but the naming pattern is otherwise mixed: some tools are verb_noun (validate_claim, suggest_questions), some are bare nouns (entity_profile, polymarket_edges), some are verbs without objects (remember, forget, ask_pipeworx), and only the five QuickBooks tools share a qb_ prefix. This creates multiple naming ecosystems with no unified convention.

Tool Count2/5

At 36 tools, this is well above the 25+ threshold for 'too many'. More importantly, the server is named Quickbooks but only 5 tools are accounting-related; the other 31 are unrelated Pipeworx data, prediction-market, memory, and meta tools, making the count both excessive and off-purpose.

Completeness2/5

For the QuickBooks domain named by the server, the surface is read-only: get customer, get invoice, list accounts, list invoices, and generic query. There are no create, update, delete, payment, bill, deposit, or report operations, which is a significant gap. For the broader Pipeworx data domain it is fairly complete, but that domain is not what the server name promises.