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Arcgis Johnsoncity

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.

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnly/openWorld/idempotent, and the description adds substantial behavioral context beyond them: internal cascading through multiple lookup endpoints, graceful degradation if GLEIF/OpenFIGI is unavailable, returning figi_candidates without asserting on ambiguity, labeling identifiers with sources, and explicitly listing unresolved identifiers in an `unresolved` field. None of this contradicts the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely dense but poorly structured — one long run-on paragraph with semicolons, especially the 'company' type section, making it hard to scan. Front-loading is decent (examples + 'Use FIRST'), but the sheer length and lack of bullet points or section breaks hurt readability. Every sentence carries value, but the organization does not.

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?

With no output schema, the description carries full responsibility for return behavior, and it delivers: it covers identifiers per type, ambiguous matches via figi_candidates, unresolved fields, source labeling, drug citation format, and degradation behavior. Input constraints and edge cases (e.g., bond names, non-US issuers) are also specified. Very little an agent needs to call it correctly is 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%, so baseline is 3, but the description massively enriches parameter meaning: the `value` parameter gets precise input constraints (pass entity name only, never the full noun phrase, bond issuer as printed) and the `type` parameter is expanded with detailed return behavior and examples. This goes far beyond the terse schema descriptions.

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 explicit query patterns and states a specific verb+resource: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It clearly differentiates itself from sibling tools by positioning itself as the ID-resolution layer that feeds other tools, which is distinct from entity_profile, compare_entities, and the research 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?

Gives an explicit trigger: 'Use FIRST whenever you have a name but need an ID.' It also provides nuanced when-to-use details for ambiguous bond names and non-equity instruments. However, it never names a specific alternative tool for when this tool is NOT appropriate, so the when-not guidance is only implied rather than explicit.

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

A3.7/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route questions to similar data sources, and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. The many polymarket tools also blur together despite detailed descriptions.

Naming Consistency3/5

Most names are snake_case, but there is no consistent verb_noun pattern: ask_pipeworx, bet_research, entity_profile, layer_info, pipeworx_trending, recent_changes, and validate_claim follow different stylistic conventions. The pattern is readable but feels like several naming vocabularies were merged.

Tool Count2/5

34 tools is well over the comfortable range and most of them are unrelated to the apparent ArcGIS Johnson City purpose. Only search_datasets, layer_info, and query_layer actually serve GIS needs; the remaining 31 tools form a sprawling Pipeworx meta-platform bolted onto the same server.

Completeness3/5

The ArcGIS portion covers discover-schema-query reasonably well for read-only open data, and the Pipeworx side has broad coverage with subscriptions, memory, feedback, and research workflows. However, the surface is defined by two unrelated domains, making it hard to judge true completeness for any one stated purpose.

Resources