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Data Sonoma

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

A4.1/5.0
Behavior5/5

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

The description richly discloses behavior beyond the annotations: it cascades through multiple lookup endpoints, degrades gracefully when GLEIF/OpenFIGI are unavailable, returns unresolved identifiers explicitly, and handles ambiguous matches by returning figi_candidates instead of asserting a single result. This adds far more than the readOnly/openWorld/idempotent hints provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

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

The description is extremely dense, with long nested parentheticals, many repeated example phrasings, and a wall-of-text structure. It is front-loaded with purpose and examples, but the sheer length and redundancy make it less scannable and less concise than it should be.

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 covers return behavior in enough detail: resolved identifiers, unresolved entries, figi_candidates, source labels, and drug-specific outcomes like RxCUI, ingredient, brand, and citation. It also addresses fallback behavior and input edge cases, so the agent has a complete picture for invoking the tool correctly.

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 coverage is 100%, but the description adds meaningful parameter context: it introduces ISIN as an accepted input, clarifies that non-equity issuers resolve here, and reinforces the entity-name-only rule for bond lookups. Some detail overlaps with the schema, but the ISIN and ambiguity handling go beyond it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool resolves a user-spoken name into canonical/official identifiers and is meant to be used first when an ID is needed. It is specific about the verb, resource, and supported entity types, but it does not explicitly differentiate itself 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?

The description gives an explicit trigger: 'Use FIRST whenever you have a name but need an ID,' and clarifies supported types and accepted input forms. However, it does not provide explicit when-not-to-use guidance or name alternative tools, so the guidance is strong but not fully exclusionary.

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 unclear boundaries: ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded reuses the same router, and deep_research overlaps with ask_pipeworx for multi-part questions. Similarly, bet_research, polymarket_edges, polymarket_arbitrage, entity_profile, compare_entities, recent_changes, and resolve_entity all cluster around overlapping research/comparison tasks despite detailed descriptions.

Naming Consistency3/5

Names are consistently lowercase snake_case, but the naming pattern is mixed: some are verb_noun (ask_pipeworx, validate_claim, resolve_entity), some are bare nouns (datasets, metadata, polymarket_edges), some are imperative verbs (remember, forget, query), and some are adjective_noun (recent_alerts, recent_changes). The polymarket_* and pipeworx_* prefixes help, but the overall convention is not uniform.

Tool Count2/5

With 34 tools, the server exceeds the 25+ threshold and feels overstuffed for a coherent single-purpose MCP server. It spans unrelated domains: Sonoma County open data, a general structured-data router, prediction-market analysis, memory, subscriptions, npm dependency checking, and llms.txt generation—each could reasonably be its own smaller server.

Completeness4/5

The core research workflow is well covered: routing, grounded verification, entity resolution, entity profiles, comparisons, recent changes, claim validation, memory, subscriptions, and prediction-market execution checks are all present. Minor gaps exist, such as no subscription update/edit, no general pipeworx:// record-reader tool, and a read-only open-data surface, but agents can usually work around these.

Resources