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Glama

Activecampaign

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

A4.6/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses internal cascading lookups, graceful degradation when GLEIF/OpenFIGI are unavailable, ambiguity handling via figi_candidates, explicit unresolved fields, and source-labeling of identifiers. This is substantial behavioral context that annotations alone do not convey.

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 a useful purpose statement and organized by supported type, but it is extremely long and includes redundant trigger-phrase examples and dense parenthetical chains. Several sentences could be tightened without losing critical nuance.

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, the absence of an output schema, and the presence of rich annotations, the description is impressively complete. It explains what each resolved identifier is, how ambiguous matches behave, what happens on enrichment failure, and what is returned for both supported types.

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 adds high-value meaning: accepted input formats, the 'entity name only' warning, the exact bond-issuer example, ISIN-to-LEI behavior, and drug-name expectations. It clarifies edge cases such as non-ticker securities resolving via FIGI rather than failing.

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 action—resolving a user-spoken name to canonical/official identifiers—and ties it to the input requirements of other tools. It also distinguishes itself by enumerating supported entity types and explicitly routing cases like bonds and non-US issuers that sibling lookup tools may not handle.

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 provides an explicit invocation condition: 'Use FIRST whenever you have a name but need an ID.' It also gives detailed guidance for tricky cases, such as passing only the issuer name for bonds. However, it does not name sibling alternatives or give explicit 'when not to use' exclusions, so it stops short of a 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

B3.3/5.0
Disambiguation2/5

Several tools occupy nearly the same role: ask_pipeworx and ask_pipeworx_beta are described as behaviorally identical, ask_pipeworx_grounded and deep_research both route questions across the same large catalog, and the Polymarket tools overlap heavily in intent. The ActiveCampaign list/get tools are distinct, but an agent facing 37 tools would frequently struggle to choose the right research or betting tool.

Naming Consistency2/5

Some clusters are internally consistent (list_*, ask_pipeworx_*, polymarket_*), but the server overall mixes bare verbs like remember and forget, noun phrases like entity_profile and deep_research, and brand-prefixed names like pipeworx_trending and polymarket_arbitrage. There is no unified naming convention across the tool set.

Tool Count2/5

37 tools is excessive for an ActiveCampaign integration, and only 6 of them actually relate to ActiveCampaign. The rest are a broad Pipeworx data, research, and prediction-market utility suite, so the count is not well-scoped to the server's stated purpose.

Completeness1/5

As an ActiveCampaign server, the surface is severely incomplete: it only provides read-only list/get operations and no create, update, delete, send, tag, or workflow-management tools. The unrelated Pipeworx tools may be feature-rich, but they do not fill the gaps in the named ActiveCampaign domain.

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