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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?

The description goes far beyond the annotations by disclosing graceful degradation, explicit unresolved identifiers, multi-match behavior returning figi_candidates, source labeling, and the cascading internal lookups. It also explains that non-equity instruments without tickers still resolve and that ISINs map to legal entities via GLEIF. No contradictions with annotations exist.

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 information-dense and front-loaded with purpose, but the single long paragraph with embedded parentheticals is somewhat harder to scan than a structured format would be. Every sentence contributes value, though breaking out supported types and edge cases could improve readability.

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 multi-source, multi-type resolver with no output schema, the description is remarkably complete. It specifies accepted inputs, return fields, ambiguity handling, unresolved-identifier behavior, and degradation paths. An agent has enough information to invoke the tool correctly in most realistic scenarios.

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?

Even though schema coverage is 100%, the description adds significant meaning: it clarifies that value must be the entity name only, not the full noun phrase, and gives concrete examples for bonds, drugs, tickers, CIKs, and ISINs. This level of guidance materially reduces the chance of an agent passing malformed input.

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 clearly states the tool's purpose: resolving a user-spoken name to canonical/official identifiers required by other tools. It differentiates itself from siblings by focusing on identity resolution rather than profiles, comparison, or queries. The opening examples and 'Use FIRST whenever you have a name but need an ID' make the resource and action 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 says when to use the tool: 'Use FIRST whenever you have a name but need an ID.' It also gives guidance on input formats and multiple-match ambiguity. However, it does not name sibling tools to avoid or provide explicit when-not-to-use conditions, so it falls just short of full exclusionary guidance.

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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but several overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical (only differing in verification/depth), and polymarket_edges / polymarket_arbitrage / polymarket_edge_tracker / polymarket_fill_risk / polymarket_kalshi_spread all target similar prediction-market signals, which could cause mis-selection without careful reading.

Naming Consistency4/5

Most names follow a clear verb_noun pattern (resolve_entity, query_table, remember, recall, forget, subscribe, unsubscribe, validate_claim, compare_entities, search_within), but there are exceptions like ai_visibility_check (adjective_noun), generate_llms_txt (verb_noun with dot), and several polymarket_* names that are fine but inconsistent with the snake_case verb-first convention.

Tool Count5/5

35 tools is a large but reasonable surface for a broad data/serach platform covering company financials, economics, prediction markets, memory, subscriptions, and discovery. The count is justified by the wide domain, and the set is not bloated with trivial duplicates.

Completeness4/5

The surface covers core CRUD for entities (resolve, profile, compare, search, query) and memory (remember/recall/forget), plus subscriptions and meta-tools. Minor gaps: no explicit tool for updating/creating entities (understandable for a read-only data service), and no tool for listing all available table schemas beyond discovery (subjects covers this). Overall strong coverage.

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