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abm_architect

Read-only

Architecte ABM — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Gapup Hub — ABM 20 comptes nommés · Budget €120k · Tier 1×5 + Tier 2×15 · Playbooks 3 niveaux. Inputs are validated server-side — send the documented case fields.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
companyYes
productYes
salesTeamNo
icpCriteriaYes
abmBudgetEurNo
targetAccountsYes
currentChannelsNo

TDQS

C2.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, indicating no mutation and flexible input. The description adds that the tool returns an 'audited deliverable' and validates inputs server-side, which aligns with read-only behavior. However, it omits other behavioral details such as rate limits, response format, or error handling.

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 concise but mixes languages (French/English) and includes a lengthy reference case that, while helpful, adds clutter. The core message is front-loaded but could be more tightly structured without the example.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 8 parameters, nested objects, and no output schema, the description is incomplete. It does not describe the deliverable structure, possible error conditions, or how to interpret results. The reference case provides partial context but leaves significant gaps for effective use.

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

Parameters2/5

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

Schema description coverage is only 13%, with most parameters lacking a description. The description does not explain individual parameters beyond a minimal reference case. The sparse schema and lack of parameter guidance means an agent would struggle to understand field semantics or 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 specifies that the tool is an ABM architect for C-suite (CMO) and returns a structured, audited deliverable. The reference case provides concrete context. However, jargon like 'Gapup agent-payable' obscures clarity, and the purpose is not sharply differentiated from sibling tools like abm_lookalike_account_finder.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description offers no guidance on when to use this tool versus alternatives, nor does it state prerequisites or when not to use it. The only instruction is to 'send the documented case fields', which is insufficient for an agent to decide between similar tools.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

Resources