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mcp-knowledge

by io.github.getgapup

press_influencer

Read-only

Presse & influenceurs — Gapup agent-payable C-suite expertise (CMO). Returns a structured, audited deliverable. Reference case: Agicap (levée Série C €70M) — CP + 12 contacts presse Tier-1 · plan de diffusion 14 jours. 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.
budgetNo
companyYes
targetMediaYes
announcementYes
targetAudienceYes

TDQS

C2.8/5.0
Behavior3/5

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

Annotations already provide readOnlyHint=true (safe read) and openWorldHint=true (potential external access). The description adds that inputs are validated server-side and returns a deliverable, but does not elaborate on internal process, side effects, or constraints. It adds some value but not rich behavioral context beyond 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 moderately concise but includes extraneous branding ('Gapup agent-payable C-suite expertise (CMO)') and a reference case that may not be universally understood. It is front-loaded with the main idea but could be more efficient.

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 the tool's complexity (6 params, nested objects, no output schema), the description is incomplete. It does not explain the deliverable's structure, how to interpret results, or error handling. The openWorldHint annotation suggests flexibility, but the description provides insufficient guidance for an agent to use it correctly.

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 very low (17%). The description does not explain any parameters, simply saying 'send the documented case fields'. It fails to add meaning to the 6 parameters, especially the nested objects. The reference case does not clarify parameter usage.

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 mentions 'Presse & influenceurs' and 'Returns a structured, audited deliverable', which hints at generating a press and influencer outreach plan. The reference case (Agicap fundraising) provides a concrete example. However, it lacks a clear verb like 'generates' or 'creates' to explicitly state the action, and the purpose is somewhat implicit.

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?

No guidance is provided on when to use this tool versus alternatives. The sibling list includes many marketing tools but no distinctions are made. The description does not mention when to use, when not to use, or any prerequisites.

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