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revenue-enablement-mcp

Proposal Section Writer

proposal_section_writer
Read-only

Generate customized proposal sections tailored to specific buyers. Creates executive summaries, solution overviews, pricing justifications, and more.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toneNoTone for the proposal (changes only the opening of the executive summary)
pricingNoPricing details if relevant
section_typeYesProposal section to generate
customer_nameNoCustomer name
your_solutionYesYour product/solution
success_metricsNoExpected outcomes/metrics
primary_audienceNoAccepted but not used yet: the text is the same for every audience
customer_industryNoCustomer industry
customer_challengesNoKey challenges identified
key_differentiatorsNoWhy you vs alternatives
implementation_approachNoHow you will implement

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.9/5.0
Behavior2/5

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 no further behavioral context such as length, determinism, or the fact that inputs like primary_audience are accepted but ignored. Its claim of 'customized'/'tailored' output is arguably overstated given the schema notes that audience-specific text is never produced.

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?

Two short sentences, front-loaded with the core action and followed by illustrative outputs. No filler or redundancy, though the second sentence is close to restating the enum.

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

Completeness3/5

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

For an 11-parameter generation tool with only 2 required params and no output schema, the description is minimal but the schema carries most of the load. It never explains which two parameters are required or what the generated section looks like, leaving a real gap around intended usage and expected output.

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 11 parameters, including the enum values and the tone caveat. The description adds only the examples of section types, which duplicate the section_type enum, so baseline 3 is appropriate.

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 a specific verb+resource ('Generate customized proposal sections') and names concrete section types (executive summaries, solution overviews, pricing justifications). It is clearly distinguishable from most siblings like win_loss_analyzer or discovery_question_bank, but it never explicitly differentiates itself from the adjacent sales-asset generators (roi_business_case_builder, demo_script_builder).

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?

There is no when-to-use guidance, no prerequisites, and no reference to any alternative tool. The phrase 'tailored to specific buyers' hints at context but stops short of telling an agent when this tool should be selected over its many siblings. The agent must infer selection criteria entirely.

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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