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Glama

Rdap

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

A4.6/5.0
Behavior5/5

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

Annotations already establish read-only, idempotent, and open-world behavior, so the description's additional context is high-value. It discloses graceful degradation of LEI/FIGI enrichment, explicit unresolved identifiers, ambiguous bond candidate behavior, and internal cascading across lookup endpoints — all 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 well-structured with opening examples, a clear usage directive, and organized type-specific paragraphs. It is dense and verbose in places, but the length is justified by the tool's complexity and the need to communicate edge cases like non-equity instruments and ambiguous matches.

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?

Despite lacking an output schema, the description thoroughly covers accepted inputs, resolution behavior, identifier sources, fallback behavior, and ambiguous-result handling. An agent has nearly everything needed to invoke the tool correctly and understand what results to expect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds significant meaning beyond the schema. It enriches the 'value' parameter with detailed formatting guidance, accepted identifier forms, the warning against full noun phrases for bonds, and clarifies type-specific behavior for company and drug.

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 ('resolve') and resource (user-spoken names to official identifiers) with concrete examples like ticker, CIK, LEI, and RxCUI. It clearly distinguishes itself as the first-step lookup tool for converting names to IDs, which differentiates it from sibling tools like entity and entity_profile.

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,' which gives direct when-to-use guidance. It does not, however, mention when to prefer sibling tools such as entity or entity_profile, so it lacks exclusions or explicit alternatives.

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

B3.1/5.0
Disambiguation2/5

The RDAP tools (domain, ip, asn, nameserver, entity) are distinct, but the larger Pipeworx collection creates significant overlap: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates, and the polymarket_* family (edges, arbitrage, edge_tracker, fill_risk, kalshi_spread) has heavily overlapping purposes. ai_visibility_check and scan_competitor_ai_presence also overlap.

Naming Consistency3/5

Most tools use snake_case with a descriptive verb_noun pattern (e.g., ask_pipeworx, discover_tools, recent_changes), but the set mixes short single nouns (domain, ip, asn, entity) with compound names, and there are oddities like generate_llms_txt and ask_pipeworx_grounded that deviate from a clear uniform convention.

Tool Count2/5

At 36 tools, the server is far heavier than expected for an RDAP service. The RDAP core only needs ~5 tools; the rest are an unrelated Pipeworx mega-suite (data queries, prediction markets, memory, subscriptions, feedback) that makes the server feel like a dumping ground rather than a focused toolset.

Completeness4/5

For the RDAP portion, coverage is complete: domain, IP, ASN, nameserver, and entity records are all present. The broader Pipeworx features also cover their own workflows (query, research, comparison, memory CRUD, subscription lifecycle), but the server's stated RDAP identity is muddied by these extras, and some integration points (e.g., no direct update for RDAP data, which is read-only anyway) are inherent limitations.

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