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Arcgis Lakecountyil

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

A4.7/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior, and the description goes well beyond them: it discloses cascading internal lookups, graceful degradation when GLEIF/OpenFIGI are unavailable, explicit `unresolved` fields, `figi_candidates` on ambiguous names, and ISIN-to-LEI mapping. There is no contradiction with 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, but this is justified by the tool's complexity: two entity types, multiple identifier sources, and ambiguity/fallback behavior. It is front-loaded with examples and the primary rule, though several parentheticals and motivational phrases could be compressed without losing meaning.

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?

Even without an output schema, the description explains what will be returned — identifiers with source labels, `unresolved` fields, `figi_candidates`, drug ingredients/brands, and citation links — plus behavior under partial service failure. An agent has enough context to decide when to use the tool, how to format inputs, and what to expect back.

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 the baseline is 3, but the description adds substantial value-parameter nuance: 'Pass the ENTITY NAME ONLY... for a bond that is the ISSUER exactly as printed... never the question's full noun phrase.' It also expands each enum value with accepted input variants and identifier outcomes, which materially changes how the value parameter should be filled.

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 names a specific action — 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input' — and names the concrete result resources: CIK, ticker, LEI, FIGI, RxCUI. The supported types and query examples make it clearly distinguishable 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 Guidelines4/5

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

'Use FIRST whenever you have a name but need an ID' is an explicit, actionable trigger, and the description enumerates accepted input forms for each type. However, it never names sibling alternatives or states conditions where another tool should be used instead, so it is clear but not fully contrastive.

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

Multiple tools have overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical, while ask_pipeworx_grounded, deep_research, validate_claim, and entity_profile all perform data lookups with only subtle differences. discover_tools and suggest_questions also serve similar onboarding/exploration roles.

Naming Consistency4/5

Tool names are uniformly snake_case and generally follow a verb_noun or noun_phrase pattern (e.g., ask_pipeworx, query_layer, entity_profile, remember). There is a slight mix between verb-first and noun-first names but no chaotic conventions like camelCase or inconsistent verb tense.

Tool Count2/5

At 34 tools, the count is well above the typical 15-25 range for a coherent server. More critically, the server is named 'Arcgis Lakecountyil' but only 3 tools (search_datasets, layer_info, query_layer) actually relate to ArcGIS; the remaining 31 tools are a broad Pipeworx data platform, creating a severe scope mismatch that makes the count feel excessive and unfocused.

Completeness2/5

For the ArcGIS domain implied by the server name, the surface is barely complete: it offers search, schema inspection, and querying, but no create, update, delete, or management capabilities. Conversely, the Pipeworx side is relatively rich, but that doesn't match the server's stated purpose, leaving the overall set incomplete for its apparent intended use.

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