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Faa 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.

TDQS

A4.7/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, openWorldHint, idempotentHint), the description discloses rich behavioral traits: graceful degradation when GLEIF/OpenFIGI is unavailable, ambiguity handling via 'figi_candidates', explicit reporting of unresolved identifiers rather than omission, and internal cascading through multiple lookup endpoints. This goes well beyond the structured annotations and materially shapes caller expectations.

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, but the length is largely justified by the tool's complexity and the number of edge cases. It is reasonably structured with uppercase section markers ('SUPPORTED TYPES', 'LEI/FIGI enrichment') and front-loaded trigger phrases. Some example phrasing is repetitive, but the density is acceptable for a tool with this many behaviors.

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 return behavior, and it does so thoroughly: resolved identifiers, source labels for each identifier, unresolved identifiers, candidate lists on ambiguity, and degradation behavior. Given the complexity of the tool and its two entity types, the description covers the essential information an agent needs to invoke it correctly and interpret results.

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 substantial semantic value beyond the schema. It gives concrete value formats (ticker 'AAPL', CIK '0000320193', ISIN examples), clarifies that company input can be ticker, CIK, ISIN, or name, and provides important caveats like passing only the issuer name for bonds and avoiding trailing security-class words. This is especially valuable for the free-text value parameter.

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 natural-language triggers ('What's the ticker for…', 'find the CIK for…'), then states a specific action: resolve a user-spoken name to canonical/official identifiers. It clearly distinguishes supported entity types ('company', 'drug') and lists the identifiers returned per type, so an agent understands exactly what this tool does and why it exists.

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 instructs 'Use FIRST whenever you have a name but need an ID,' which is strong when-to-use guidance. It also explains that the tool replaces 2-3 manual lookups. However, it does not explicitly contrast this with sibling tools like entity_profile or compare_entities, so exclusionary guidance is implicit rather than explicit.

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
Disambiguation2/5

Multiple tools have unclear boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same 5,714-tool catalog with heavily overlapping purposes, and ask_pipeworx_beta is currently identical to ask_pipeworx. Similarly, bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, and polymarket_fill_risk all target prediction-market analysis and could easily be confused by an agent. The two FAA tools (faa_regulation, faa_search) are distinct, but they are buried among a dozen unrelated data-lookup and memory tools.

Naming Consistency4/5

Most tools follow a consistent lowercase snake_case verb_noun or noun_verb pattern (faa_search, resolve_entity, compare_entities, validate_claim, discover_tools, unsubscribe). Minor deviations exist, such as ask_pipeworx and pipeworx_feedback lacking underscores, and the polymarket_* family mixes noun-led names, but overall the naming is readable and predictable.

Tool Count1/5

33 tools for a server named 'Faa Regulations' is a severe mismatch: only 2 of the 33 tools (faa_regulation, faa_search) relate to FAA regulations, with the rest covering general data lookups, prediction markets, SEC filings, memory storage, npm dependency checking, and llms.txt generation. The count is far too high for the stated domain, and most tools do not belong in this server at all.

Completeness2/5

The actual FAA surface is thin: faa_search provides keyword lookup and faa_regulation returns full text or a part's section list, so basic citation-lookup workflows work, but there is no update/amendment tracking, no browse-by-part navigation beyond a section list, and no related aviation data such as NOTAMs or TFRs. The dominant Pipeworx tool family is unrelated to FAA regulations, so an agent using this server for its apparent purpose would hit dead ends quickly.

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