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Crontab

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 description discloses rich behavioral detail beyond the annotations: graceful degradation when GLEIF or OpenFIGI is unavailable, refusal to assert when multiple FIGI candidates match, explicit reporting of unresolved identifiers, and source-labeling of each resolved identifier. It also notes internal cascading through multiple endpoints, which is valuable context annotations do not 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 long but front-loaded with usage examples and a crisp 'Use FIRST' directive. While the parenthetical-heavy sentences require careful reading, nearly every clause carries operational information, and the SUPPORTED TYPES structure provides useful organization for a complex tool.

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 comprehensively covers both entity types, accepted inputs, resolution sources, fallback behavior, ambiguity handling, and common failure modes. An agent receives enough guidance to invoke the tool correctly and to interpret key outcomes such as figi_candidates and unresolved.

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 substantial semantic guidance: the 'value' parameter is clarified with the directive to pass only the entity name, with examples, and an explicit warning that trailing security-class words will break FIGI matching. It also explains how the 'type' parameter routes to different canonical ID sources.

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 user-phrase examples and states a specific mission: resolving a user-spoken NAME to canonical identifiers that other tools require as input. It explicitly enumerates supported entity types and distinguishes this tool's role as an ID-resolution entry point from sibling tools like entity_profile and 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?

It gives a direct trigger condition: 'Use FIRST whenever you have a name but need an ID,' and frames the tool as replacing multiple manual lookups. It stops short of explicitly naming when-not-to-use alternatives, but the guidance is clear enough for an agent to select this tool over siblings in the common name-to-ID case.

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 overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer factual questions with subtle differences that are not immediately clear. Additionally, five polymarket tools cover similar ground (arbitrage, edges, fill risk, spread), making it hard to pick the right one without reading the full descriptions.

Naming Consistency4/5

Tool names are consistently snake_case and mostly follow a verb_noun pattern (e.g., describe_cron, next_runs, validate_claim). Minor deviations exist (bet_research, entity_profile, pipeworx_trending) but the overall style is predictable and readable.

Tool Count2/5

33 tools is well over the high end for a focused server, and the server name 'Crontab' implies a narrow cron utility while most tools are a broad data-research platform. This mismatch makes the count feel bloated and poorly scoped.

Completeness2/5

For a cron server, the set is severely incomplete: only describe_cron and next_runs exist, with no create/delete/update functionality. For the actual data-research domain, it is rich but lacks clear CRUD coverage for many resources, and the inclusion of unrelated cron/memory tools creates dead ends.

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