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Arcgis Delawareco

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

A4.9/5.0
Behavior5/5

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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses several non-obvious behaviors: cascade through multiple lookup endpoints, graceful degradation if GLEIF/OpenFIGI are unavailable, returning figi_candidates when a name doesn't uniquely identify a bond, and explicit reporting of unresolved identifiers. This adds significant context an agent needs to correctly interpret results.

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 but information-dense. It front-loads the core purpose and usage guidance, then details supported types and edge cases. Some sentences are dense with parentheses and technical detail, so it's not perfectly concise, but every sentence earns its place given the tool's complexity.

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 tool with no output schema and only 2 params, the description fully equips an agent to call it correctly: it explains available entity types, input formats, resolution behavior, fallbacks, and result fields (figi_candidates, unresolved). There are no obvious gaps in what an agent needs to know.

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?

The description enriches both parameters beyond the schema. For 'type' it explains the difference between company and drug resolution and what each returns. For 'value' it gives concrete examples and warns against including full noun phrases for bond issuers. This is exactly the kind of parameter-level guidance that prevents misuse.

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 user-spoken names to canonical/official identifiers required by other tools. It provides specific query examples (ticker, CIK, LEI, RxCUI) and names the resource type ('company' and 'drug'), making it distinct from sibling tools like entity_profile or compare_entities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Use FIRST whenever you have a name but need an ID,' giving a clear directive. It also details supported input types (ticker, CIK, ISIN, name, brand/generic drug name) and provides guidance for edge cases like passing only the entity name for bonds. This is direct, actionable usage 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

A3.5/5.0
Disambiguation2/5

Many tools have overlapping or redundant purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical in routing, and several polymarket tools (polymarket_edges, polymarket_arbitrage, polymarket_fill_risk) all surface trading opportunities with similar outputs. The three ArcGIS tools are distinct but buried among dozens of unrelated data/meta tools, making selection confusing.

Naming Consistency3/5

All names use snake_case, which is consistent, but the verb/noun pattern is inconsistent. Some are verb_noun (query_layer, resolve_entity), some are noun phrases (entity_profile, polymarket_edges, recent_alerts), and some are bare verbs (recall, remember, forget, subscribe). The naming style is readable but not predictably patterned.

Tool Count1/5

34 tools is already high, but the severe issue is that only 3 of them (search_datasets, query_layer, layer_info) relate to the server's stated ArcGIS Delaware County purpose. The other 31 are Pipeworx data, memory, subscription, and prediction-market tools, which is a blatant scope mismatch. The tool count is not appropriate for the advertised server domain.

Completeness1/5

For an ArcGIS Delaware County GIS server, the surface is extremely thin: only search, query, and layer metadata exist. There are no tools for editing features, uploading data, managing layers, or exporting maps. Conversely, the Pipeworx tools form a broad but fragmented domain with many monitoring and meta-tools but no clear end-to-end workflow. The set is severely incomplete for its apparent dual purpose.

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