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Nass

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. Changed3 schema fields changed
    • changedInput schema / properties / type / description
      Previous value: -"Entity type. v1 supports \"company\"."New value: +"Entity type: \"company\" or \"drug\"."
    • changedInput schema / properties / type / enum
      Previous value: -[
-  "company"
-]New value: +[
+  "company",
+  "drug"
+]
    • changedInput schema / properties / value / description
      Previous value: -"Ticker, CIK, or company name (e.g., \"AAPL\", \"0000320193\", \"Apple\")."New value: +"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\")."
  3. Added

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 read-only and idempotent, so the description's job is to add context — and it does. It reveals graceful degradation of LEI/FIGI enrichment, the explicit `unresolved` field, source-labelled identifiers, and the internal cascading lookup behavior. This goes well beyond what annotations alone convey.

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 front-loaded with query patterns and the explicit 'Use FIRST' directive, and it is organized by supported type. It is long and dense, with some nested parenthetical detail that could be trimmed, but almost every sentence carries useful operational information.

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?

With no output schema, the description must explain return behavior, and it does: canonical identifiers, source labels, `unresolved`, `figi_candidates`, and RxCUI plus ingredient/brand for drugs. Edge cases like ambiguous bond names, ISINs for non-US issuers, and enrichment degradation are all covered, making the tool fully callable.

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?

The schema already describes both parameters, so the baseline is 3, but the description adds high-value semantics. It explains accepted input formats for `value`, warns against full noun phrases for bonds, documents ISIN-to-LEI mapping, and specifies the type-specific behaviors for `type`. This meaningfully exceeds schema coverage.

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 operation — resolving user-spoken names to canonical or official identifiers — and gives concrete query examples for both supported types. This clearly distinguishes it from sibling tools like entity_profile or compare_entities, which focus on entities rather than identifier resolution.

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,' which is strong, actionable guidance. It also documents when the tool returns candidates for ambiguous matches, but it does not explicitly name alternative tools or give exclusions for cases where a different sibling would be preferable.

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

A3.9/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, especially the specialized ones like nass_crop_progress and bet_research. However, there is potential confusion between ask_pipeworx, ask_pipeworx_grounded, deep_research, and bet_research, as they all involve querying structured data. The descriptions do attempt to differentiate them, but the overlap could still cause misselection.

Naming Consistency4/5

Tool names consistently use lowercase with underscores and follow patterns like descriptive prefixes (nass_, pipeworx_, polymarket_) and action verbs (generate_, resolve_, validate_). Minor inconsistencies exist, such as 'search_within' versus 'discover_tools', but overall the naming is predictable and clear.

Tool Count3/5

With 36 tools, the server is on the heavier side but still fits within a reasonable range for a comprehensive data platform. The tools cover a wide variety of domains (agriculture, finance, prediction markets, memory, etc.), and each tool appears to add value. However, the count is borderline high, and some tools like ask_pipeworx could potentially replace many specialized ones.

Completeness4/5

The tool surface is comprehensive for its purpose: answering questions over structured data with support for analysis, comparison, monitoring, and feedback. There are minor gaps, such as the lack of a direct update mechanism for subscriptions beyond canceling, but the core CRUD operations are covered. The inclusion of meta-tools like ask_pipeworx and deep_research fills many potential gaps.

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