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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 mark this as read-only, idempotent, and non-destructive, and the description adds substantial behavioral detail: cascading internal lookups, graceful degradation when GLEIF/OpenFIGI are unavailable, explicit `unresolved` reporting, and returning `figi_candidates` on ambiguous matches. This goes well beyond what annotations convey.

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 purpose and usage rule are front-loaded, and every sentence adds operational detail. However, the supported-types section is a dense, sprawling paragraph with heavy parentheticals; it is informative but could be structured into cleaner bullets for easier parsing.

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 two-parameter tool with no output schema, the description covers inputs, output identifiers, ambiguity behavior, failure modes, enrichment degradation, and source provenance. Nothing needed to select or call the tool correctly appears to be missing.

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 adds critical usage nuance beyond the schema: accepted input forms (ticker, CIK, ISIN, name), ISIN-to-LEI behavior, and the explicit guidance to pass only the entity name rather than the full question phrase ('never the question's full noun phrase'). This meaningfully improves correct invocation.

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 queries ('What's the ticker for…', 'find the CIK for…') and states the core function: resolving a user-spoken name to canonical identifiers other tools require. It enumerates supported entity types (company, drug) and the identifier families returned, making the tool's role distinct and unmistakable.

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,' providing a clear triggering condition. It does not name sibling tools to avoid or contrast with, so it stops short of full exclusion guidance, but the usage context is strong.

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

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, and deep_research all route to the same 5,529 tools; polymarket_arbitrage and polymarket_edges both find tradeable opportunities; discover_tools and suggest_questions both serve discovery. The beta tool being an exact duplicate makes misselection highly likely.

Naming Consistency3/5

Most action tools follow verb_noun (ask_pipeworx, compare_entities, discover_tools, list_groups, resolve_entity, search_datasets, suggest_questions, validate_claim), but there is significant mixing with noun_noun (dataset_details, entity_profile, organization_details, pipeworx_feedback, polymarket_arbitrage) and adjective_noun (deep_research, recent_alerts). The Polymarket family is consistently prefixed, but overall the server mixes several conventions.

Tool Count2/5

36 tools far exceeds the typically well-scoped range, and the server bundles what appear to be five separate concerns: Italian open data, Pipeworx universal query, entity/report utilities, prediction-market analytics, and meta/memory/subscription features. Many tools could be consolidated (e.g., ai_visibility_check and scan_competitor_ai_presence; discover_tools and suggest_questions), making the set feel bloated.

Completeness4/5

The broad domain of structured data research and prediction-market edge is largely covered: universal routing, grounded answers, deep research, entity resolution, profiles, comparisons, change feeds, claim verification, arbitrage scans, fill-risk, subscriptions, memory, and feedback. Minor gaps exist—no direct tool to fetch raw CKAN resource URLs, no exhaustive list of all 5,529 tools, and no actual order execution on prediction markets—but these are workable around.

Resources