Skip to main content
Glama

newton

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. Changed3 schema fields changed
    • changedInput schema / properties / type / description
      Previous value: -"Entity type. v1 supports \"company\"."New value: +"Entity type: \"company\" or \"drug\"."
    • changedInput schema / properties / type / enum
      Previous value: -[
-  "company"
-]New value: +[
+  "company",
+  "drug"
+]
    • changedInput schema / properties / value / description
      Previous value: -"Ticker, CIK, or company name (e.g., \"AAPL\", \"0000320193\", \"Apple\")."New value: +"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\")."
  3. Added

TDQS

A4.7/5.0
Behavior5/5

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

The annotations already declare readOnly/openWorld/idempotent/non-destructive behavior, and the description adds substantial value beyond that: internal cascading of lookups, graceful degradation when GLEIF/OpenFIGI are unavailable, explicit `unresolved` reporting, ambiguous-name `figi_candidates` behavior, and ISIN-to-legal-entity mapping. No contradiction with annotations exists.

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 front-loaded with the core purpose and example queries, and nearly every sentence carries substantive information. It is quite long, and the 'company' section is a dense run-on parenthetical that could be better structured, but there is little fluff or redundancy.

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?

Even without an output schema, the description covers the key return behaviors: canonical identifiers per type, source labels, `unresolved` fields, `figi_candidates` for ambiguous matches, and fallback behavior for degraded enrichment. Given the tool's multi-source complexity, the description provides enough context for correct selection and 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 a baseline of 3 applies, but the description goes well beyond the schema: it explains exact input formats (ticker, CIK, ISIN, name), gives examples, and warns against passing full noun phrases like 'NEW YORK ST DORM AUTH revenue bonds.' This is actionable prescriptive guidance that materially reduces invocation errors.

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 action—resolving user-spoken names to canonical identifiers—with concrete resource types (company, drug) and named sources (SEC EDGAR, GLEIF, OpenFIGI, RxNorm). It also distinguishes itself from sibling tools by framing it as the first step for obtaining IDs that other tools require.

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 advises 'Use FIRST whenever you have a name but need an ID' and gives numerous example queries and accepted input forms. It does not explicitly name alternatives or state when not to use the tool, but the strong first-step framing and supported-type detail make the usage context clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All newton toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, such as multiple ways to get entity information (ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile, compare_entities) and numerous prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.). Despite detailed descriptions, the boundaries are unclear, making it difficult for an agent to distinguish between them.

Naming Consistency4/5

Tool names mostly follow a snake_case convention and are generally descriptive. Minor inconsistencies exist, such as 'discover_tools' vs. 'scan_competitor_ai_presence' and a few single-word verbs like 'derive' and 'remember'. Overall, the pattern is predictable and readable.

Tool Count2/5

With 34 tools, the server is heavy for a single server. The scope is very broad, covering math, memory, data retrieval, prediction markets, and more. While each tool has a specific purpose, the high count suggests a lack of focus and could overwhelm an agent.

Completeness4/5

The server offers extensive coverage for data retrieval, entity lookup, comparison, research, and prediction markets. Minor gaps exist, such as missing advanced math operations (e.g., solving equations) and some niche data sources, but the core workflows are well-covered with tools like ask_pipeworx and deep_research.

Resources