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Glama

Healthdata

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

A4.7/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses many real behaviors: cascading through multiple lookup endpoints, graceful degradation when GLEIF or OpenFIGI is unavailable, explicit `unresolved` reporting, ambiguity handling via `figi_candidates`, and provenance labeling of each identifier. This is substantial, non-obvious behavioral context.

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 and information-dense, but nearly every clause adds value. It is front-loaded with the key usage instruction and examples. Some sentences are heavily nested and could be split for readability, but nothing feels extraneous.

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 tool with two parameters, rich annotations, and no output schema, the description covers all necessary ground: supported types, accepted inputs, ambiguity behavior, unresolved-field semantics, and failure degradation. An agent can confidently decide when to call it and what to pass.

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?

Although schema coverage is 100%, the description adds crucial meaning: it defines what each enum value resolves, gives concrete input examples, and warns that only the entity name should be passed (e.g., omitting 'revenue bonds'). This materially improves the agent's chance of passing correct arguments.

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 and resource: resolving user-spoken names into canonical identifiers that other tools require as input. It clearly differentiates the tool from sibling tools by positioning it as the name-to-ID lookup layer, and the opening examples make the purpose unmistakable.

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 trigger: 'Use FIRST whenever you have a name but need an ID.' It also covers supported entity types and accepted input formats. It does not explicitly name alternative tools or state when not to use it, so it stops just short of full when/when-not guidance.

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

A3.8/5.0
Disambiguation3/5

Most tools have clearly described distinct purposes, but a few near-duplicates exist: ask_pipeworx and ask_pipeworx_beta are functionally identical right now, and ai_visibility_check vs scan_competitor_ai_presence overlap. The polymarket sub-family also has multiple edge/fill tools that could be confused.

Naming Consistency3/5

The naming is varied but readable. Many tools follow verb_noun (ask_pipeworx, resolve_entity, search_within, subscribe), yet several are noun phrases (entity_profile, recent_changes, polymarket_edges, bet_research, pipeworx_feedback). There's no single coherent pattern, but the mixture is not chaotic.

Tool Count2/5

At 33 tools, the server is oversized for typical MCP coherence. The breadth is broad (prediction markets, healthcare datasets, memory, subscriptions), but such a large count forces agents to filter through many utilities (suggest_questions, discover_tools, generate_llms_txt) that could be consolidated or hidden.

Completeness4/5

The domain—data querying and analysis—is well covered: universal routing (ask_pipeworx), grounded verification, deep research, entity resolution, dataset metadata, subscriptions, prediction-market edge checks, and memory tools. Minor gaps exist (e.g., no direct health-care-specific analytics batch or file download), but no major dead ends for core workflows.

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