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Glama

Fda Regulations

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 annotations (readOnly, openWorld, idempotent), the description discloses important non-obvious behaviors: graceful degradation when GLEIF/OpenFIGI are unavailable, ambiguous name matches returning `figi_candidates`, unresolved identifiers surface under `unresolved` instead of being omitted, and every identifier is source-labelled. This is rich behavioral context that materially helps an agent anticipate output.

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 front-loaded with the core purpose and usage directive, and the two entity types are organized under SUPPORTED TYPES. However, it is quite long and contains dense parenthetical run-ons; most sentences earn their place given the complexity, but a cleaner bulleted structure would make it more consumable.

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 2-parameter tool with no output schema, the description covers inputs, edge cases (multiple matches, unresolved identifiers), provider fallback behavior, and output characteristics (`figi_candidates`, `unresolved`, source labels). Nothing needed for correct selection and invocation appears to be missing.

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?

While the schema already describes both parameters, the description adds significant meaning beyond it: ISIN is a valid `value` input for company (not mentioned in the schema), drug accepts brand or generic names, and there is critical guidance to pass only the issuer name for bonds, never the full noun phrase. This substantially enriches the parameter semantics.

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 precise verb and resource: it resolves a user-spoken name to canonical identifiers (ticker, CIK, LEI, RxCUI, FIGI), with explicit example phrasings. It clearly differentiates itself as the pre-tool for obtaining IDs that other tools require as input, which distinguishes it from siblings like entity_profile or 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 explicitly says 'Use FIRST whenever you have a name but need an ID,' giving a clear trigger condition, and adds that it replaces 2-3 manual lookups. However, it does not name sibling alternatives or explicitly state when-not-to-use (e.g., when an ID is already available), so the guidance is clear but lacks explicit exclusions.

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
Disambiguation1/5

The tool set contains near-duplicate query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and many overlapping accessors (deep_research, validate_claim, fda_search, fda_regulation). The server name 'Fda Regulations' is also misleading because the vast majority of tools (e.g., polymarket_*, generate_llms_txt, remember) have nothing to do with FDA regulations, making correct selection extremely difficult.

Naming Consistency2/5

Most names use snake_case, but the verb/noun pattern is inconsistent: some are verb-first (ask_pipeworx, generate_llms_txt, validate_claim), some are noun-first (entity_profile, recent_changes, pipeworx_trending), and the ask_pipeworx_beta/grounded variants break the convention. Some names are also semantically misleading (scan_dependency checks an npm package rather than scanning a dependency).

Tool Count1/5

With 33 tools, this is far too many for a server nominally about FDA regulations; only two tools directly address that domain. Even as a general-purpose data platform, 33 tools is excessive and includes many unrelated utilities (e.g., generate_llms_txt, scan_dependency), making the server's scope unclear and bloated.

Completeness2/5

For the stated FDA regulations purpose, only fda_regulation (get by citation) and fda_search (keyword search) exist, providing basic read coverage but no access to FDA data (drug labels, adverse events, recalls), guidance documents, or regulatory history. The many unrelated tools do not fill these gaps, so the surface is severely incomplete for its apparent purpose.

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