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Data Oakland

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

A4.7/5.0
Behavior5/5

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

Annotations already mark the tool as read-only, open-world, idempotent, and non-destructive. The description adds substantial behavioral context beyond those annotations: graceful degradation of LEI/FIGI enrichment, returning figi_candidates instead of asserting when multiple instruments match, explicitly listing unresolved identifiers instead of omitting them, and cascading through multiple lookup endpoints.

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 every section earns its place: user-facing trigger examples, usage positioning, supported types, resolution behavior, enrichment caveats, and input format warnings. It is front-loaded with the core purpose and key usage directive before diving into detailed edge cases.

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, the description is remarkably complete: it describes what identifiers each entity type returns, source labeling, unresolved handling, multi-match behavior, enrichment degradation, and even cites the internal pip URI for drug concepts. An agent has enough context to choose and invoke this tool correctly across both supported types.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3, but the description adds valuable parameter semantics beyond the schema: passing the entity name only, using the issuer name exactly as printed for bonds, avoiding trailing security-class words, and accepting ticker, CIK, ISIN, or company name as input. This meaningfully reduces misuse risk.

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 (resolve) and resource (user-spoken names to canonical/official identifiers), and explicitly positions the tool as the source of IDs that other tools require as input. It clearly covers supported entity types and distinguishes itself from sibling tools by emphasizing identifier resolution as a gateway step.

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 guidance: 'Use FIRST whenever you have a name but need an ID.' It also clarifies supported input types (ticker, CIK, ISIN, company name, drug name). However, it does not explicitly state when not to use this tool or name alternatives such as entity_profile or compare_entities, so it falls just short of full alternative routing.

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

A3.8/5.0
Disambiguation2/5

Several tool clusters have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical today, with ask_pipeworx_grounded and deep_research routing through the same 5,756-tool catalog, and validate_claim falling into the same grounded pipeline. The five polymarket_* tools plus bet_research all target prediction-market opportunities with overlapping outputs (edge_pp vs gap_pp vs spread_pp), and compare_entities/entity_profile/recent_changes share the same SEC/XBRL/news fan-out. The verbose descriptions help, but the set itself would frequently misroute an agent.

Naming Consistency3/5

All names are uniformly snake_case with no casing mixing, and the ask_pipeworx_*, polymarket_*, and pipeworx_* prefixes create recognizable families. However, the set mixes verb_noun names (validate_claim, list_subscriptions), bare verbs (query, recall, forget), and noun-phrase names (entity_profile, recent_alerts, datasets, metadata), so there is no single predictable pattern across the server.

Tool Count3/5

34 tools is heavy, but the server's scope is genuinely enormous: it is a gateway to 5,756 tools across 1,504 sources, plus prediction-market analysis, subscriptions, and memory. The count is defensible for that scope, yet several tools (generate_llms_txt, scan_dependency, ai_visibility_check, the memory trio) are peripheral to the core data mission, giving the set a scattershot feel and preventing a well-scoped rating.

Completeness4/5

The core data-research workflow is thoroughly covered: casual lookup (ask_pipeworx), grounded verification (ask_pipeworx_grounded, validate_claim), deep research (deep_research), entity resolution and profiling (resolve_entity, entity_profile, compare_entities, recent_changes), and a six-tool prediction-market suite. Subscriptions and memory have full lifecycles, and Oakland data offers search, schema, and query. Minor gaps exist — no subscription update/pause, no raw dataset export, and no write path for Oakland data — but no advertised workflow hits a dead end.

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