revenue-enablement-mcp

Proposal Section Writer

proposal_section_writer

Read-only

Helps you write one section of a proposal for a named buyer. You give the section you need (such as an executive summary, solution overview or pricing justification), your solution, the buyer's challenges, your differentiators, pricing and success measures. You get that section in sentences built from your inputs, with sector notes. It invents no figure, customer or date, and says once which inputs were not given.

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": {
      "section_type": "<section_type>",
      "your_solution": "<your_solution>"
    },
    "name": "proposal_section_writer"
  }
}

Input Schema

Name	Required	Description
`tone`	No	Tone for the proposal (changes only the opening of the executive summary)
`pricing`	No	Pricing details if relevant
`your_sector`	No	Optional: your company's own industry (not your customers'). When given, it decides the sector notes; when left out, the tool reads it from your words and stays neutral if they are unclear.
`section_type`	Yes	Proposal section to generate
`customer_name`	No	Customer name
`your_solution`	Yes	Your product/solution
`success_metrics`	No	Expected outcomes/metrics
`primary_audience`	No	Adds a one-line note at the top of the executive summary on what this audience looks for
`customer_industry`	No	Customer industry
`customer_challenges`	No	Key challenges identified
`key_differentiators`	No	Why you vs alternatives
`implementation_approach`	No	How you will implement

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedOct 18, 2026

changedInput schema / properties / primary_audience / description

Previous value: -"Accepted but not used yet: the text is the same for every audience"New value: +"Adds a one-line note at the top of the executive summary on what this audience looks for"

addedInput schema / properties / your_sector

Added value: +{
+  "description": "Optional: your company's own industry (not your customers'). When given, it decides the sector notes; when left out, the tool reads it from your words and stays neutral if they are unclear.",
+  "enum": [
+    "logistics_tech",
+    "fintech",
+    "saas",
+    "vertical_saas",
+    "ai_native",
+    "ites",
+    "telecom",
+    "software",
+    "cybersecurity",
+    "other"
+  ],
+  "type": "string"
+}

First observedOct 17, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds genuinely non-obvious behavior beyond that: it invents no figure, customer or date, it appends sector notes, and it reports missing inputs exactly once — all useful for trusting the output. It does not describe length or formatting of the returned section, which keeps it below a 5.

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

Conciseness4/5

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

Three sentences, front-loaded with the core action and then the input/output contract, with no filler or marketing language. The middle sentence is a dense comma list, but every item maps to a real parameter, so it earns its space.

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 12 parameters, full schema coverage and no output schema, the description does the necessary work: it describes the shape of the return (prose built from the inputs plus sector notes) and the missing-input disclosure. What remains unstated is output length, structure, and whether the section is returned alone or with surrounding context — minor for a read-only generator.

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 12 parameters including enum values and the note that tone affects only the executive summary opening. The description's input list largely restates those fields; its only added semantic value is that omitted sector input is inferred from the user's words and stays neutral when unclear, which is also present in the schema. Baseline 3 applies.

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?

States a specific verb and resource — 'write one section of a proposal for a named buyer' — and enumerates example section types (executive summary, solution overview, pricing justification), which separates it from siblings like roi_business_case_builder or demo_script_builder. It stops short of explicitly naming an alternative tool, so it is clear but not maximally differentiated.

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 implies the usage context by listing the inputs to supply (section, solution, buyer challenges, differentiators, pricing, success measures), which tells an agent roughly when the tool applies. It never states when NOT to use it or points to a sibling for adjacent jobs such as a full business case or a demo script, so routing is left to inference.

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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