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Arcgis Eagan

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 cover read-only/idempotent behavior, and the description adds substantial behavioral context: cascading internal lookups, graceful degradation when GLEIF/OpenFIGI unavailable, explicit `unresolved` reporting, ambiguous-name handling via `figi_candidates`, and ISIN-to-LEI mapping. This goes far beyond what annotations provide.

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 information-dense and front-loaded with examples, but it is a long wall of text with deeply nested parentheticals, especially in the 'company' section. Breaking it into structured bullets or shorter sentences would improve scannability without losing content.

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 complex tool with no output schema, the description explains input constraints, supported entity types, identifier sources, ambiguity behavior, unresolved results, and fallback behavior. An agent has enough context to invoke it correctly and interpret what it returns.

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%, but the description still substantially enhances parameter meaning: it provides concrete input examples (AAPL, 0000320193, ozempic), explains enum semantics, and warns against passing full noun phrases like 'revenue bonds' because it breaks FIGI matching. This is valuable semantics beyond the schema.

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 uses specific verbs and resources: resolving user-spoken names into canonical identifiers for company and drug, with explicit identifier types (CIK, LEI, FIGI, RxCUI). It clearly differentiates itself from sibling tools by stating it is the first step whenever a name but no ID is available.

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 'Use FIRST whenever you have a name but need an ID', giving clear conditions for use, and notes it replaces 2-3 manual lookups. However, it does not explicitly contrast with sibling tools like entity_profile or validate_claim, so exclusion conditions are left 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

A3.5/5.0
Disambiguation2/5

Several tool groups overlap heavily: the three ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) differ mainly in guarantees, and six Polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) all target the same trading-concept space with fuzzy boundaries. While ArcGIS, memory, and subscription tools are distinct, an agent will frequently struggle to pick the right research or analysis tool.

Naming Consistency2/5

All names are snake_case, but the structural pattern is inconsistent. Some are verb-first (query_layer, search_datasets, validate_claim), others are noun-first or domain-prefixed (entity_profile, layer_info, polymarket_edges, recent_alerts, pipeworx_feedback). There is no predictable verb_noun convention across the set, making it hard to guess a tool's name from its function.

Tool Count2/5

At 34 tools, the set is well above the typical focused-server range, and the server name 'Arcgis Eagan' suggests a narrow GIS purpose while only 3–4 tools are actually ArcGIS-related. The remaining ~30 tools form a broad, unrelated utility collection (Pipeworx data, memory, subscriptions, trending, npm scanning), making the count feel bloated and unfocused.

Completeness2/5

For the primary ArcGIS domain, only search, layer inspection, and querying are supported — there are no create, update, delete, or editing tools, leaving obvious lifecycle gaps. Meanwhile, the Pipeworx side is over-stocked with redundant analysis tools, and the overall mix lacks a coherent coverage story for any single stated purpose.

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