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Entalpa Requirements MCP

Projects Make Description

projects_make_description

Draft a project description for the supplied stakeholder. Save accepted text with projects_patch using design_description. Returns a proposal without applying the suggested changes. Review it with the user, then save accepted changes using the existing CRUD tools. May take several minutes; progress notifications report elapsed time when supported.

Input Schema

NameRequiredDescriptionDefault
project_idYesProject UUID (database id).
stakeholderYesStakeholder name or role to address in the description.
user_messageNoOptional instructions to guide this AI operation.
extra_requirement_attributesNoList of extra requirement fields.

Output Schema

NameRequiredDescriptionDefault
descriptionYesProject description or design notes.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Adds real behavioral context beyond the annotations: the result is a non-applied proposal, saving happens through a separate tool, and the call can take several minutes with progress notifications. There is mild tension with readOnlyHint=false (the tool may persist generated content), but the description never claims pure read-only behavior, so it is not a contradiction.

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?

Front-loaded with the core action, then the persistence guidance, then the latency caveat. Five short sentences, all earning their place, though the saving workflow could be tightened slightly.

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?

An output schema exists so return values need not be described, and the description covers the action, the non-applied nature of the result, the save path, and latency. Complete enough to invoke correctly, with only the sibling-routing guidance left implicit.

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 baseline is 3; every parameter including stakeholder, user_message, and extra_requirement_attributes is already documented in the schema. The description names the design_description field used for saving, but adds no syntax or format detail beyond what the schema provides.

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?

States a specific verb+resource ('Draft a project description') and immediately clarifies the output is a proposal rather than an applied change, which separates it from siblings like projects_check_description (validates) and projects_make_stories_from_description (derives stories). An agent can identify what this produces without opening the schema.

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?

Gives concrete usage flow: draft, review with the user, then persist accepted text via projects_patch with design_description. It names the save path and the review step, but never states when NOT to use it or how it differs from adjacent generators such as projects_specify_design or projects_generate.

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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