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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 annotations already mark the tool as read-only and idempotent, and the description adds substantial behavior beyond those hints: internal cascading lookups, graceful degradation when GLEIF/OpenFIGI is unavailable, the figi_candidates behavior for ambiguous matches, explicit unresolved identifiers, and source-labeling of every identifier. No contradiction exists between the description and 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 and dense, but it is organized: examples first, then the use-first directive, supported types, caveats, and degradation behavior. Some parentheticals are heavy, but the flow is logical and the critical guidance is front-loaded.

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?

Given the tool has no output schema, the description covers what the agent needs: supported types, accepted inputs, return contents (identifiers, sources, unresolved, figi_candidates), and failure/degradation behavior. It is complete enough to invoke correctly across both company and drug 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?

Although schema coverage is 100%, the description significantly enriches the parameters: it explains that value must be the entity name only, warns against trailing security-class words for bonds, gives concrete examples for both type values, and clarifies accepted identifiers like ticker, CIK, ISIN, or company name. This materially helps the agent pass correct values.

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 and a clear statement: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It distinguishes the tool from siblings by framing it as the first-stop identifier lookup and enumerating supported entity types, so an agent can immediately tell it apart from profile or comparison 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 explicitly says 'Use FIRST whenever you have a name but need an ID,' which gives a clear trigger condition. It also describes the accepted input forms and the company/drug split, but it does not explicitly list when-not-to-use or name alternative sibling tools, leaving some exclusion guidance implicit.

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
Disambiguation3/5

Several tools cluster around the same core purpose: the three ask_pipeworx variants, the three census reverse-geocoders, and the six Polymarket analysis tools. Descriptions are detailed enough to disambiguate most choices, but ask_pipeworx_beta is currently identical to ask_pipeworx, creating genuine ambiguity. An agent could easily select the wrong tool in these overlapping families.

Naming Consistency3/5

Names mix verb-initial actions (ask_pipeworx, compare_entities, resolve_entity) with noun-initial compound names (census_block, entity_profile, polymarket_edges). The polymarket_* family is internally consistent, but the set as a whole lacks a uniform verb_noun convention. Single-word verbs like remember, recall, and forget further break the pattern.

Tool Count2/5

At 34 tools, this exceeds the 'too many' threshold of 25 and includes clear redundancy: ask_pipeworx_beta duplicates ask_pipeworx, county_for_point is a thin wrapper over the same service as census_area/census_block, and scan_competitor_ai_presence just loops ai_visibility_check. The broad scope does not justify this many tools, and the set would be better split into focused servers.

Completeness4/5

Each sub-domain has solid lifecycle coverage: memory has remember/recall/forget, subscriptions have subscribe/list/unsubscribe/recent_alerts, and company research has resolve_entity/entity_profile/compare_entities/recent_changes. Minor gaps exist (e.g., no direct pipeworx:// citation-fetching tool), but no critical dead ends that would cause agent failures.

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