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Glama

Fcc 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

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, but the description adds significant behavioral context: it cascades through multiple lookup endpoints, asserts nothing on ambiguous matches (returns figi_candidates), labels identifiers by source, explicitly lists unresolved identifiers, and explains how ISINs resolve to legal entities. These details are not present in annotations and substantially increase transparency about the tool's behavior.

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 information-rich, starting with examples and the core purpose, then detailing supported types and edge cases. It is not overly verbose for the tool's complexity, but it is a long paragraph that could be slightly streamlined (e.g., listing examples in a more scannable way). It is appropriately front-loaded with the primary use case.

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?

There is no output schema, so the description must explain return values, and it does so thoroughly: it mentions identifiers are labeled with source, unresolved identifiers are under 'unresolved', multiple matches return 'figi_candidates', and graceful degradation when external services fail. Combined with the parameter detail and usage guidance, the agent has a complete picture for correct invocation.

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 parameters are already documented, but the description enriches meaning beyond the schema. For the 'value' parameter, it provides formatting rules (pass entity name only, not full noun phrase) with concrete examples (e.g., 'NEW YORK ST DORM AUTH' vs 'NEW YORK ST DORM AUTH revenue bonds'). For 'type', it details what each type returns (CIK, ticker, LEI, FIGI, RxCUI) and acceptance criteria (ticker, CIK, ISIN, or name). This goes well beyond the schema's basic enums and 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 query examples ('What's the ticker for...') and then states the core function: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It clearly distinguishes from siblings by emphasizing it is the first stop for name-to-ID resolution and enumerates two supported types (company, drug) with explicit sub-details. This is specific, actionable, and differentiates it from other tools 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 Guidelines5/5

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 a clear directive. It further clarifies when not to use certain behaviors (e.g., for bonds, pass the issuer exactly as printed, not the full noun phrase) and explains fallback behavior (graceful degradation when GLEIF/OpenFIGI unavailable). This provides strong guidance on when and how to invoke the tool, including what to avoid.

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

A3.6/5.0
Disambiguation2/5

Several tool clusters have poorly defined boundaries: ask_pipeworx and ask_pipeworx_beta are currently functionally identical, the five polymarket tools all orbit 'find/validate trading edges', and ai_visibility_check overlaps heavily with scan_competitor_ai_presence. The memory and subscription tools are distinct, but the core query/edge clusters would cause frequent misselection.

Naming Consistency2/5

Naming conventions are mixed across the set: the ask_pipeworx* family uses verb+product, the polymarket_* family uses domain-prefixed nouns, fcc_regulation and fcc_regulations_search differ in singular/plural and lack a verb, and tools like discover_tools, entity_profile, and generate_llms_txt each follow different patterns. No single predictable convention holds across the server.

Tool Count2/5

33 tools is heavy on its own, but the count is especially inappropriate given the server is named 'Fcc Regulations': only 2 of the 33 tools actually serve FCC regulatory text, while the other 31 tools belong to a general-purpose Pipeworx data-query platform. The set is far too broad for the declared scope.

Completeness2/5

For the stated FCC-regulations purpose, the two relevant tools cover search and full-text retrieval, but there is no change tracking, no notification of rule updates, no historical/version comparison, and no adjacent FCC filings/licensing data despite the server name implying broader FCC coverage. The extra 31 unrelated tools do not fill these gaps, so an agent expecting FCC completeness would hit dead ends.

Resources