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Denue

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

A4.7/5.0
Behavior5/5

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

The annotations only say the tool is read-only, open-world, idempotent, and non-destructive. The description adds substantial behavior: internal cascading lookups, explicit unresolved identifiers, source labeling, ambiguous-match handling, graceful degradation when GLEIF/OpenFIGI are unavailable, and coverage of non-equity instruments and non-US issuers. No contradiction with annotations exists.

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 the core use case and priority instruction, but it is lengthy and dense with parentheticals. Most content is informative for a complex tool, yet the repeated paraphrase list and some meta-commentary could be trimmed without losing meaning.

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 carries the burden of explaining return semantics, and it does: it lists resolved identifiers per type, the `unresolved` field, `figi_candidates` for ambiguous matches, source labeling, and fallback behavior. Given the tool's complexity and multiple entity types, nothing essential for correct invocation is missing.

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?

Although schema coverage is 100%, the description goes well beyond the schema. For `value`, it warns to pass the entity name only, not the full noun phrase, and gives a bond issuer example with a correlated edge case. For `type`, it explains what 'company' and 'drug' resolve to and which input forms are accepted.

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: resolve a user-spoken name to canonical/official identifiers. It immediately distinguishes itself from sibling lookup tools by saying it should be used 'FIRST whenever you have a name but need an ID' and enumerates supported types ('company', 'drug') with concrete identifier outputs.

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 clear context: 'Use FIRST whenever you have a name but need an ID.' It also explains when ambiguity should be handled by returning figi_candidates and when enrichment degrades gracefully. However, it does not explicitly name sibling alternatives or state when not to use this tool.

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

A4/5.0
Disambiguation4/5

Tools are mostly distinct with detailed descriptions guiding usage, though some overlap exists between similar query tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research, which could cause confusion for agents not reading carefully.

Naming Consistency3/5

All tool names use snake_case and are readable, but there is no consistent verb-noun pattern. Prefixes vary widely (actions like ask, compare, generate vs. domains like denue, polymarket), making the naming scheme inconsistent.

Tool Count3/5

With 35 tools, the count is high for a server named 'Denue', which suggests a narrower focus. While the breadth may be justified by the platform's capabilities, the number feels slightly heavy and could be streamlined.

Completeness4/5

The tool set covers a wide range of data sources and operations, but it is read-only with no write capabilities for external data. Memory and subscription tools add some action, but overall, it is fairly complete for its stated purpose.

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