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uluesky

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. Added

TDQS

A4.4/5.0
Behavior5/5

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

The description discloses behavior beyond the readOnly/openWorld/idempotent annotations: it cascades through multiple endpoints, returns figi_candidates on ambiguous matches, explicitly reports unresolved identifiers, and degrades gracefully when GLEIF/OpenFIGI are unavailable. This significantly helps an agent predict side effects and edge-case behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

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

The definition is front-loaded with examples and a clear 'Use FIRST' directive, and nearly every clause carries real information. However, the body is a very long single paragraph with deep parenthetical nesting, making it harder to scan than its content warrants.

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?

There is no output schema, so the description's explicit mention of returned data (CIK, ticker, LEI, ownership, FIGI, figi_candidates, unresolved, RxCUI, ingredient, brand, citation) fills that gap. It also covers accepted input forms and failure/degradation behavior, making the tool safe to invoke with only two parameters.

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 the schema already documents both parameters, the description adds critical operational meaning: company accepts ticker, CIK, ISIN, or name; drug accepts brand or generic. The warning to pass only the entity name for bonds — because trailing security-class words match nothing — is essential guidance not present in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input' names the verb and resource precisely, and the supported-type examples make scope clear. It does not explicitly contrast a sibling tool such as resolve_handle or entity_profile, so it lacks the strongest sibling differentiation.

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?

'Use FIRST whenever you have a name but need an ID' is an explicit trigger condition, and the supported types/examples clarify when the tool applies. It does not name alternative tools or state when not to use it, stopping short of the full 5.

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
Disambiguation3/5

Most tools have clearly distinct roles, but several overlapping pairs create ambiguity: ask_pipeworx vs ask_pipeworx_beta are explicitly identical today, discover_tools vs suggest_questions both serve discovery/onboarding, and bet_research vs polymarket_edges both address betting-edge questions. The detailed descriptions help, but an agent could still select the wrong tool in these cases.

Naming Consistency2/5

The set uses at least four naming conventions: get_* for Bluesky reads, verb_noun for Pipeworx tools (ask_pipeworx, resolve_entity, validate_claim), polymarket_* prefixed tools, and verb-only memory tools (remember, recall, forget). Each subgroup is internally consistent, but the overall mix feels inconsistent and unpredictable.

Tool Count2/5

39 tools is well beyond the typical well-scoped server, and the scope sprawls across Bluesky reads, Pipeworx data, Polymarket analysis, memory, subscriptions, and one-off utilities like generate_llms_txt and scan_dependency. The count would be more reasonable split into separate servers; as-is it feels heavy and unfocused.

Completeness4/5

Within the server's evident scope, coverage is strong: Bluesky read operations, Pipeworx query/research/verification, entity profiling, and subscription lifecycle are all represented. The main gaps are write actions for Bluesky (posting, following, liking) and a few auxiliary features that are only partially integrated, but no critical workflow dead-ends appear for the primary data-research use cases.

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