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Market Recap

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?

Annotations already declare read-only and idempotent behavior, and the description adds substantial non-obvious context: the call cascades through several lookup endpoints, LEI/FIGI enrichment degrades gracefully, ambiguous matches assert nothing and return figi_candidates, and unresolved identifiers are explicitly listed rather than omitted. 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.

Conciseness4/5

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

The description is long and dense, but most of the length is earned by non-obvious lookup semantics, examples, and caveats. It is front-loaded with purpose and trigger phrases; minor redundancy and sprawling parentheticals keep it from a perfect structure score.

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 resolution outcomes, ambiguity handling, source labeling, unresolved fields, degradation behavior, and supported types. An agent has what it needs to invoke the tool correctly and interpret key result states.

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?

Schema coverage is 100%, but the description adds meaning far beyond the schema: it explains the 'pass the entity name only' rule with a counterexample, enumerates accepted company inputs (ticker, CIK, ISIN, name), and clarifies ISIN-to-LEI behavior for non-US issuers. This materially helps an agent format parameters correctly.

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 opens with concrete trigger phrases and a precise verb-resource pair: resolving user-spoken names to canonical identifiers. It enumerates supported types (company/drug), states what each returns, and clearly separates itself from sibling lookup/profile tools by framing itself as the identity-resolution entry point ('Use FIRST whenever you have a name but need an ID').

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 trigger examples and a strong 'Use FIRST whenever you have a name but need an ID' directive, plus input-format guidance. It does not explicitly name or contrast specific sibling alternatives, but the usage context and priority are clear enough for an agent to route correctly.

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

Many tools overlap in purpose (e.g., multiple Polymarket analysis tools, multiple AI visibility tools, ask_pipeworx vs deep_research). Agents will have difficulty choosing the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names use a mix of styles (snake_case, descriptive phrases) without a consistent verb_noun pattern. For example, 'ask_pipeworx' and 'bet_research' have different naming conventions. This inconsistency makes it harder for agents to predict tool names.

Tool Count2/5

32 tools is on the high side for a single server. Many tools could be merged (e.g., multiple polymarket tools). The count feels excessive for the scope, causing cognitive load and potential selection errors.

Completeness3/5

The tool set covers a wide range of domains (prediction markets, company data, fact-checking, etc.) but has notable gaps (e.g., limited entity types for company/drug only). Redundancy in some areas makes the set feel bloated rather than complete.

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