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Glama

Fda Devices

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/idempotent annotations, the description discloses important runtime behavior: ambiguous matches return figi_candidates for disambiguation, unresolved identifiers are surfaced under unresolved rather than omitted, identifiers are labelled with their source, and GLEIF/OpenFIGI degradation still returns EDGAR identifiers. This is substantial transparency beyond what annotations provide.

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 and front-loaded with examples and the core instruction. Some parenthetical chains are long and slightly awkward, and a few sentences are promotional or redundant, but nearly every clause carries useful operational detail, so the length is mostly earned.

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 burden of explaining outputs, and it does: it lists returned identifiers, sources, candidate lists, unresolved fields, and graceful degradation. It also covers both supported entity types, input formats, and non-US/ISIN edge cases, making it complete for an agent to select and invoke 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?

Although schema coverage is 100%, the description adds significant semantic value: accepted forms for value include ticker, CIK, ISIN, or company name; drug inputs accept brand or generic names; and it warns to pass the issuer name exactly as printed for bonds, never the full noun phrase. This materially reduces invocation errors.

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: it resolves user-spoken names to canonical/official identifiers required by other tools. It also maps concrete query patterns ('ticker for', 'CIK for', 'LEI for') to the tool's behavior, making the purpose unmistakable and differentiating it from downstream lookup tools.

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,' giving clear contextual guidance and implying it precedes tools like entity_profile or compare_entities. It does not name those alternatives explicitly or state when not to use the tool, but the usage context is still clear.

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

The FDA-specific tools are distinct, but they are mixed with many generic Pipeworx tools (e.g., ask_pipeworx variants, deep_research, entity_profile) that have overlapping purposes. This creates significant ambiguity for an agent trying to choose the right tool for FDA-related queries.

Naming Consistency2/5

Tool names follow two inconsistent patterns: FDA tools use 'fda_device_*' (consistent), while generic tools use various patterns like 'ask_pipeworx', 'deep_research', 'remember', etc. The mix of snake_case, camelCase, and descriptive phrases lacks coherence.

Tool Count2/5

With 37 tools, the count is high for what is intended as an FDA devices server. Only 6 tools are directly FDA-related; the rest are generic and dilute the purpose. The scope is mismatched, making the tool count inappropriate.

Completeness3/5

The FDA tools cover key areas: 510k search, adverse events, PMA, recalls, company profiles. However, the server is incomplete for its name because it lacks many tools that a comprehensive FDA devices server would have, and the generic tools don't fill those gaps.

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