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mcp-knowledge

by io.github.getgapup

battle_plan

Read-only

Plan de bataille marketing — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Gapup Hub — Q3 2026 · Budget €120k · Pipeline €800k · 5 chantiers prioritaires. 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.
quarterYes
teamSizeYes
arrTargetYes
budgetEurYes
arrCurrentYes
companyNameYes
topChannelsYes
icpDescriptionYes
currentBlockersYes
primaryObjectiveYes

TDQS

B3/5.0
Behavior3/5

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

Annotations indicate readOnlyHint=true and openWorldHint=true, so the tool is likely a read-only computation. The description adds that inputs are validated server-side and returns a deliverable, but does not detail authentication, rate limits, or whether the deliverable is cached. No contradiction with annotations.

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 relatively short but includes a verbose reference case that may not be necessary for general use. It front-loads the purpose, but the case adds clutter without enhancing clarity.

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?

With 11 parameters, no output schema, and no details on the deliverable's structure, the description is incomplete. The reference case provides a concrete example but does not generalize well. Annotations only partially compensate.

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 9%, with only the 'async' parameter described. The description does not elaborate on the 11 parameters or their expected formats, leaving agents to infer from the reference case. With low coverage, the description fails to compensate.

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 states it returns a 'structured, audited deliverable' for a marketing battle plan, citing a specific reference case. While the verb is implied ('creates' or 'generates'), it lacks explicit differentiation from sibling tools like 'growth_path_architect' or 'market_entry_strategist'.

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

Usage Guidelines3/5

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

The description mentions that inputs must be validated server-side and to send the documented case fields, implying a structured input scenario. However, it does not specify when to use this tool versus alternatives, nor does it provide context for exclusion criteria.

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