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Alchemy Eth

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?

Beyond the annotations (readOnly, openWorld, idempotent), the description discloses ambiguous-match behavior (`figi_candidates`), explicit `unresolved` results, graceful degradation when GLEIF/OpenFIGI are unavailable, source labelling, and internal cascading lookups. This is rich behavioral context with no contradiction of 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 but front-loaded with purpose and organized into supported types and behaviors. Nearly every sentence conveys a non-obvious constraint or output trait; the density is justified by the tool's complexity, though some parentheticals could be trimmed.

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 carry return-shape information, and it does: named candidates, unresolved fields, source labels, and enriched ownership/identifier data. For a two-parameter tool with this complexity, the agent has what it needs to call and interpret the result.

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%, and the description still adds substantial value: it explains accepted formats for `value` with examples, warns to pass only the entity name and not the full noun phrase, and clarifies how ISIN inputs map to legal entities. The `type` enum is also expanded with supported entities.

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 ('resolve') and resource (user-spoken names to canonical/official identifiers) and enumerates supported entity types with concrete query examples. This clearly distinguishes it from sibling lookup/search tools, especially with the 'other tools require as input' framing.

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 explicit usage context: 'Use FIRST whenever you have a name but need an ID,' supported types, and example phrasings. It doesn't name siblings to avoid or list exclusion conditions, but the guidance is clear enough to route an agent.

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

C2.7/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta (currently described as identical), ask_pipeworx_grounded, and deep_research all serve question-answering/research, and the six Polymarket tools heavily overlap in finding and evaluating trading edges. Individual descriptions are detailed, but an agent could easily route to the wrong variant.

Naming Consistency2/5

The set mixes conventions: Pipeworx tools mostly use verb_noun (ask_pipeworx, compare_entities, resolve_entity), but memory tools are bare verbs (remember, recall, forget), and the Ethereum tools are inconsistent (nft_metadata vs nfts_owned vs nft_owners, token_balances vs token_allowance). The lack of a uniform pattern makes the surface harder to predict.

Tool Count2/5

At 40 tools, the server is oversized for the apparent core purpose of an Alchemy Ethereum interface. There is also significant redundancy: multiple ask/deep-research entry points and a dense suite of Polymarket analysis tools add bulk that could be consolidated.

Completeness2/5

The Ethereum side is mostly read-only convenience wrappers (NFTs, tokens, asset transfers) plus a generic eth_call catch-all, but lacks dedicated transaction sending, block/transaction detail, logs, or ENS conveniences. The rest of the tool surface is a sprawling collection of unrelated data-research, memory, and subscription features, making the overall implied domain incoherent and likely to leave obvious gaps for users expecting a focused Ethereum server.

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