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Glama

Stripe_connect

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. Changed3 schema fields changed
    • changedInput schema / properties / type / description
      Previous value: -"Entity type. v1 supports \"company\"."New value: +"Entity type: \"company\" or \"drug\"."
    • changedInput schema / properties / type / enum
      Previous value: -[
-  "company"
-]New value: +[
+  "company",
+  "drug"
+]
    • changedInput schema / properties / value / description
      Previous value: -"Ticker, CIK, or company name (e.g., \"AAPL\", \"0000320193\", \"Apple\")."New value: +"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\")."
  3. Added

TDQS

A4.6/5.0
Behavior5/5

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

The description discloses rich behavioral detail beyond the annotations: cascading internal lookups, graceful degradation when GLEIF/OpenFIGI is unavailable, candidate-list behavior on ambiguous matches, explicit unresolved fields, and source labeling. None of this contradicts the readOnlyHint, openWorldHint, idempotentHint, or destructiveHint annotations, and it gives an agent a reliable picture of side-effect-free behavior.

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 core trigger and supported types are front-loaded, and the description is clearly organized by SUPPORTED TYPES. It is dense and could be easier to scan if the long company parenthetical were broken into bullets, but every sentence contributes operational detail rather than filler.

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?

Given the tool's complexity, no output schema, and rich annotations, the description is remarkably complete: it covers accepted input formats, supported entity types, multi-match behavior, unresolved identifiers, source labeling, ISIN-to-LEI resolution, and graceful degradation. An agent has enough context to invoke the tool correctly and interpret its results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3, but the description adds meaning beyond the schema by introducing ISIN as an accepted company input, explaining that an ISIN resolves to the issuing legal entity, and describing ambiguous-match behavior via figi_candidates. This extra semantic context improves an 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 opens with concrete user-phrase examples and immediately states the verb + resource: resolve a user-spoken NAME to canonical/official identifiers. It enumerates supported types (company, drug) and ID systems (CIK, LEI, FIGI, RxCUI), and it makes the tool's role as the precursor to other tools explicit, which distinguishes it from siblings 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?

The description gives an explicit trigger condition: 'Use FIRST whenever you have a name but need an ID.' It also explains that the tool replaces 2-3 manual lookups, which helps an agent understand when it is the right step. It does not explicitly name sibling alternatives or state when not to use it, so it falls just short of full when/when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Stripe_connect toolsInspect
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TDQS

B3.3/5.0
Disambiguation2/5

The set has several near-duplicate entry points: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, while ask_pipeworx_grounded/deep_research and bet_research/polymarket_edges overlap heavily. Verbose descriptions help in isolation, but an agent must choose between many similar-looking research and prediction-market tools before it can act.

Naming Consistency2/5

Naming mixes bare verbs (remember, forget, subscribe), prefixed families (pipeworx_*, polymarket_*, stripe_*), and descriptive noun-style names (entity_profile, validate_claim) with no single verb_noun pattern. Each cluster is internally consistent, but the overall set is unpredictable.

Tool Count2/5

37 tools is excessive for a server named Stripe_connect, especially since only 6 tools are actually Stripe-related and the rest are a sprawling Pipeworx data-research and prediction-market stack. The count would be heavy even for the broad research domain, and it is a serious scope mismatch for the stated name.

Completeness1/5

For the Stripe domain implied by the server name, the surface is severely incomplete: it is read-only (get/list) with no way to create customers, take payments, issue refunds, update invoices, or manage subscription lifecycles. The non-Stripe research tools are broad, but that does not fill the payment workflow gap.

Resources