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Gnews

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?

Annotations already declare read-only/idempotent/non-destructive behavior; the description adds significant extra context: internal cascaded lookups, graceful degradation if LEI/FIGI sources are down, explicit unresolved identifiers, source-labelled outputs, and ambiguity handling via figi_candidates. No contradiction with annotations.

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 densely packed and organized by supported type, with trigger examples front-loaded and caveats placed where relevant. A few parenthetical asides could be trimmed, but all content contributes to correct invocation.

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 complex, multi-source lookup tool with no output schema, the description covers input variants, return behavior (labelled identifiers, unresolved list, candidates), failure modes, and cross-source coverage. An agent has enough to call it correctly in varied use cases.

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?

Though the schema documents both parameters at 100%, the description adds essential meaning beyond it: accepted aliases for value (ticker, CIK, ISIN, name), the 'entity name only' constraint with bond-specific examples, ISIN-to-LEI resolution semantics, and the distinction between brand and generic drug names.

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?

States a specific verb ('resolve') and resource ('user-spoken NAME to canonical/official identifiers'), enumerates trigger phrases, and clearly differentiates from siblings like entity_profile/compare_entities by focusing on ID resolution rather than analysis. The two supported types are named with examples.

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?

Explicitly instructs 'Use FIRST whenever you have a name but need an ID' and specifies supported entity types and accepted inputs. It does not explicitly name exclusion cases or alternative sibling tools, but the context is unambiguous enough for an agent to select it.

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

A3.9/5.0
Disambiguation4/5

Most tools have distinct, well-described purposes, but there is some overlap, especially among prediction market tools (bet_research, polymarket_arbitrage, etc.) and between ask_pipeworx and ask_pipeworx_grounded. Agents might occasionally select the wrong tool without careful reading.

Naming Consistency3/5

Tool names follow a mix of snake_case and camelCase (e.g., ai_visibility_check vs discover_tools). Some names are descriptive but inconsistent in style (subscribe, unsubscribe, list_subscriptions). Pattern is not uniform.

Tool Count3/5

With 32 tools, the server covers many domains (news, financials, prediction markets, entity resolution, memory). While each tool has a justification, the count feels heavy for a single server, and some tools could be consolidated.

Completeness3/5

The tool set spans a wide range of data sources and operations, but there are notable gaps. For news, only search and top headlines exist without advanced filtering. Prediction markets lack order placement tools. The broad scope means depth is sacrificed in some areas.

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