Vouch

get_share_kit

Return ready-to-post LinkedIn, X/Twitter, and newsletter blurbs a founder can copy-paste to announce what they learned validating an idea with Vouch. Use when the user says they finished interviews, want to 'build in public', 'share results', 'post an update', or 'tell people what I learned'. Provide the founder's headline (idea in one sentence) and the top insight or number they want to highlight.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "headline": "<headline>",
      "insight": "<insight>"
    },
    "name": "get_share_kit"
  }
}

Input Schema

Name	Required	Description
`insight`	Yes	The single most compelling insight/number from validation, e.g. '8 of 10 chefs said they'd pay $49/mo, but only 2 for real-time alerts'.
`headline`	Yes	One-sentence framing of the idea, e.g. 'AI copilot for pastry chefs'.
`projectId`	No	Optional Vouch project id to link the public report card.
`referralCode`	No	Optional 8-char Vouch referral code to append so the founder earns credit and a Pro trial for every signup from the post.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 12, 2026

TDQS

A4/5.0

Behavior3/5

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

With no annotations, the description carries the full burden. It discloses the output artifact—copy-paste social and newsletter blurbs tied to Vouch validation—but does not explain side effects, permissions, generation behavior, or how optional parameters affect the result.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three tight sentences, front-loading the output and then adding usage triggers and required inputs. Every sentence contributes useful information without repetition.

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

Completeness4/5

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

With no output schema, the description adequately explains the return artifact, supported channels, and trigger conditions. It could be more explicit about the optional linkage and referral code effects, but it is sufficient for correct invocation.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all four parameters, including optional projectId and referralCode. The description adds framing for headline and insight but does not add meaning beyond the schema for the optional parameters.

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 verb and resource: returns ready-to-post LinkedIn, X/Twitter, and newsletter blurbs for announcing validation learnings. This clearly distinguishes it from sibling tools like get_methodology or start_validation.

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 gives explicit trigger phrases such as 'finished interviews', 'build in public', 'share results', and 'post an update', which clearly indicate when to use the tool. It does not mention when not to use it or name alternative tools, so it falls short of full when/when-not guidance.

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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