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Opensea

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. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Even with annotations already declaring readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior, the description adds substantial behavioral detail: it explains ambiguous matches return figi_candidates, unresolved identifiers are explicitly listed, enrichment degrades gracefully when GLEIF/OpenFIGI are unavailable, and each identifier is source-labelled. 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 long and dense, but nearly every sentence contributes unique guidance—input examples, supported types, ambiguity behavior, degradation semantics, and the replacement value of the tool. It is somewhat run-on and could benefit from more structure, but it is appropriately detailed for a complex resolver with two entity types.

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 thoroughly covers what the agent needs: input formats, return contents including figi_candidates, unresolved identifiers, source labels, and graceful degradation. The cascading internal lookup behaviour is also disclosed, making the tool's behaviour predictable even without a formal output specification.

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 goes far beyond the schema by detailing accepted input forms per type, giving concrete examples, and explaining the important caveat that bond lookups require the issuer name exactly as printed without trailing security-class words. This materially improves the agent's ability to pass the right value.

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: it resolves a user-spoken NAME to canonical/official identifiers required by other tools. It lists concrete query patterns and supported entity types, making the tool's purpose unambiguous and clearly distinct from siblings like entity_profile or 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?

The description gives explicit guidance: "Use FIRST whenever you have a name but need an ID," which clearly tells the agent when to select this tool. It does not explicitly name alternative tools or state when not to use it, but the standalone purpose and examples provide clear context.

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

A3.6/5.0
Disambiguation2/5

Tools cluster into overlapping groups: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates distinguished only by mode; bet_research, polymarket_edges, and polymarket_arbitrage all target prediction-market opportunities. The five OpenSea read tools are distinct, but they are buried among several unrelated domains, making misselection likely.

Naming Consistency4/5

Names are overwhelmingly snake_case with a verb_noun structure (get_collection, list_owned_nfts, validate_claim, create nothing but still remember/unsubscribe). Pipelined families like ask_pipeworx_* and polymarket_* are consistent, with only minor deviations such as pipworx_trending or bet_research not following a clear verb-object pattern.

Tool Count2/5

36 tools is too many for a coherent server, and the count is inflated by at least four unrelated domains: OpenSea NFT reads, Pipeworx data lookup/research, Polymarket betting, and memory/subscription utilities. Only five tools actually relate to the server's stated OpenSea purpose, so the surface is heavily bloated with off-scope functionality.

Completeness2/5

The OpenSea-relevant tools cover basic read operations—collections, stats, single NFT, collection NFTs, and owned NFTs—but omit search, events, offers/listings, order book data, and account/contract details. The many unrelated Pipeworx tools do not fill these gaps, so an agent needing real marketplace behavior would hit dead ends.

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