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Glama

Arcgis Abbotsford

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

A4.8/5.0
Behavior5/5

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

The annotations already mark the tool as read-only/idempotent, and the description adds substantial behavioral context: internal cascading of multiple lookup endpoints, graceful degradation for LEI/FIGI enrichment, explicit unresolved-identifier handling, and non-equity instrument resolution. It also clarifies that ambiguous multi-match instrument names return figi_candidates instead of asserting a wrong answer. 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.

Conciseness5/5

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

The description is long but front-loaded with purpose and usage, then organizes details by supported types. Every sentence adds operational value, particularly the caveats about bonds, ISIN-to-LEI resolution, and unresolved identifiers. The density is justified by the tool's complex behavior and broad coverage.

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's complexity and the absence of an output schema, the description is unusually complete. It covers input forms, supported entity types, resolution sources, ambiguity handling, failure behavior, and enrichment fallback. An agent has enough information to select the tool and form a valid resolve_entity call without needing external documentation.

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?

Even though schema description coverage is 100%, the description adds significant parameter-level meaning. It explains what each type supports, gives input examples for ticker/CIK/ISIN/name and drug brand/generic names, and provides crucial advice to pass only the entity name, never the full noun phrase, because trailing security-class words will fail the FIGI lookup.

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 states a specific verb and resource: it "resolve[s] a user-spoken NAME to the canonical/official identifiers other tools require as input." It also gives concrete utterance examples and distinguishes itself as the identifier-resolution tool among siblings like entity_profile and compare_entities by framing its output as input for other 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?

The description gives explicit usage context: "Use FIRST whenever you have a name but need an ID," and it enumerates trigger phrases such as "what's the ticker for…" and "look up the ID for…". It does not name sibling alternatives or explicitly state when not to use it, so it misses full exclusionary 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

A3.6/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and discover_tools all provide data lookup/research. The PolyMarket family (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) also creates boundary confusion despite varied signals.

Naming Consistency3/5

Most tools use snake_case, but the naming pattern is mixed: some are imperative verb phrases (query_layer, validate_claim), while others are noun phrases (entity_profile, recent_alerts, layer_info). Descriptions are readable overall, but there is no consistent verb_noun convention across the set.

Tool Count2/5

34 tools is high and the vast majority are unrelated to the server's stated ArcGIS Abbotsford purpose. The set appears to be a generic Pipeworx data/prediction-market toolkit with only a few GIS-specific tools, making the count excessive for the declared scope.

Completeness2/5

For the ArcGIS Abbotsford domain, the surface is severely incomplete: only search_datasets, query_layer, and layer_info cover GIS functionality, lacking update/delete/create operations or broader dataset management. For the actual Pipeworx domain, coverage is decent, but that does not match the server name.

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