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Glama

Ipify

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

A4.8/5.0
Behavior5/5

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

Annotations already declare the call safe/read-only/idempotent, and the description adds substantial behavior: cross-source identity assembly (SEC EDGAR, GLEIF, OpenFIGI), ambiguous matches returning figi_candidates, unresolved items listed under unresolved rather than omitted, and graceful degradation when GLEIF/OpenFIGI are unavailable. This goes well beyond the structured hints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Despite its length, the description is front-loaded with examples and the 'Use FIRST' directive, then organized by type ('company', 'drug'). Each detail (ambiguous candidates, unresolved, degradation) earns its place; there is no filler or tautology.

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 full burden and delivers: it names return artifacts (CIK, ticker, company_name, LEI, FIGI, RxCUI), source labeling, unresolved behavior, and an explicit output form (figi_candidates). Edge cases like non-US issuers and non-equity instruments are also covered, so an agent can call it correctly without external lookup.

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%, but the description deepens value semantics: it adds ISIN as accepted company input, explains the drug type returns RxCUI + ingredient + brand, and warns to pass the exact issuer name rather than a full noun phrase. These details are critical to getting a bond/instrument lookup right.

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 user phrasings ('What's the ticker for…', 'find the CIK for…') and states a specific verb+resource: resolving a user-spoken NAME into canonical identifiers. It distinguishes itself from sibling tools by positioning itself as the FIRST step 'whenever you have a name but need an ID', i.e., the ID provider for other 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?

It gives an explicit trigger: 'Use FIRST whenever you have a name but need an ID.' It also catalogues the exact query forms the tool handles. However, it never names sibling tools to use instead (entity_profile, compare_entities) or states explicit exclusions, so alternative routing is implied rather than fully formalized.

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

Multiple tools overlap conceptually, e.g., ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded / deep_research all perform data retrieval, and bet_research / polymarket_edges / polymarket_arbitrage cover prediction markets with unclear boundaries. This overlap forces agents to carefully read descriptions to pick the right tool.

Naming Consistency3/5

Naming is mostly snake_case but inconsistent in pattern: some are verb_noun (compare_entities), some are noun_verb (ai_visibility_check), and some are bare nouns (ipv4) or bare verbs (forget). While readable, the lack of a uniform pattern reduces predictability.

Tool Count2/5

33 tools is high for a single server, especially given the server name 'Ipify' which implies a simple IP lookup service. The broad range (from memory ops to prediction market analysis) suggests the tool set is a collection of utilities rather than a coherent, scoped API.

Completeness2/5

The tool set lacks focus: for a server named 'Ipify', basic IP geolocation or ASN lookup is missing. As a general toolkit, it covers many areas superficially but has significant gaps (e.g., no tools for updating or deleting data, no domain-specific lifecycle).

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