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Glama

N8n

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

A4.4/5.0
Behavior5/5

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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds substantial behavioral detail beyond this: graceful degradation when GLEIF/OpenFIGI are unavailable, ambiguous matches returning `figi_candidates`, explicit `unresolved` fields, and source labelling for each identifier. This is exactly the kind of beyond-annotations context that helps an agent trust the tool's failure modes.

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 description is front-loaded with the primary use case and trigger phrase, but the body is a long, dense paragraph with nested parentheticals and overlapping detail. It contains valuable information, yet structure could be improved with clearer separation between type-specific behavior, input rules, and output behavior.

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?

Even without an output schema, the description explains what each type returns: CIK, ticker, company name, LEI, FIGI, unresolved identifiers, and drug RxCUI/brand/ingredient data. It also covers edge cases such as ambiguous bond issuer matches and partial enrichment failures. For a complex lookup tool with two entity types and multiple upstream sources, this is remarkably complete.

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%, so the baseline is 3, but the description meaningfully enriches parameter understanding. For `value`, it adds the critical guidance 'Pass the ENTITY NAME ONLY' and explains why trailing security-class words break bond lookups. For `type`, it details accepted input forms including ISIN and the ISIN-to-LEI mapping, going well beyond the schema's plain enum values.

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 description gives a specific verb and resource: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It enumerates supported types and example queries, so an agent can understand the tool's core function clearly. However, it does not explicitly name a sibling tool to distinguish against, relying on 'Use FIRST whenever...' instead of naming an alternative.

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 is explicit about when to use the tool: 'Use FIRST whenever you have a name but need an ID.' It also explains that it replaces multiple manual lookups, giving clear context for adoption. It does not provide explicit when-not-to-use guidance or named alternatives, so it misses the top criterion.

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

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same underlying data catalog, and the polymarket_* tools have closely related edge/arbitrage/fill-risk purposes. The descriptions are detailed, but an agent still faces real selection risk between near-duplicate query entry points.

Naming Consistency3/5

The set is consistently snake_case but otherwise mixes conventions: n8n_ and polymarket_ prefixes, bare verbs like remember/forget/recall, noun phrases like recent_alerts, and brand-style names like ask_pipeworx. It is readable but lacks a unified naming system across the server.

Tool Count2/5

34 tools is a heavy surface, especially for a server named N8n where only 3 tools actually relate to n8n workflow management. Most tools serve Pipeworx data lookup, prediction markets, memory, and subscriptions, making the server feel like several different products merged into one.

Completeness3/5

The data research, entity resolution, memory, and subscription surfaces are well covered, including useful meta-tools for discovery and grounding. However, the n8n portion is read-only with no create/update/delete/run workflow tools, and one-off utilities like generate_llms_txt and scan_dependency sit isolated, leaving the server's overall domain incomplete.

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