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Entalpa Requirements MCP

Projects Align To Story

projects_align_to_story

Propose stakeholders for an existing story identified by UUID, including a new stakeholder if needed. Merge accepted links with existing relationships when saving. 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
story_idYesStory UUID.
project_idYesProject UUID (database id).
user_messageNoOptional instructions to guide this AI operation.

Output Schema

NameRequiredDescriptionDefault
stakeholder_idsNo
new_stakeholder_nameNoStakeholder name or role.
new_stakeholder_descriptionNoOptional stakeholder description.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Adds meaningful behavior beyond the annotations: it returns a proposal without applying changes, merges accepted links with existing relationships on save, and may run for minutes with progress notifications. It also implicitly clarifies the tension with readOnlyHint=false by stating that no suggested changes are applied. This is rich disclosure, though the persistence semantics of the 'proposal' itself remain slightly unclear.

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?

Four sentences, purpose front-loaded, each carrying distinct information (proposal semantics, merge behavior, next step, latency). The progress-notification clause is marginally extraneous but overall it is tight and well ordered.

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 a rich output schema present, the description needn't explain return values, and the annotations cover the safety profile. It fills the remaining gaps by describing the async/long-running nature, the non-application guarantee, and the follow-up save path. Complete enough for an agent to call and act on it correctly.

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 all three parameters (story_id, project_id, user_message) are already documented in the schema. The description adds the notion of 'including a new stakeholder if needed' but no parameter-level syntax or format detail beyond the schema, so 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: 'Propose stakeholders for an existing story identified by UUID.' This clearly distinguishes it from the alignment siblings (requirements-to-stories, stakeholders-to-stories) by naming the input (a story UUID) and the output (stakeholder proposals). It stops short of explicitly naming which sibling to prefer, so it is clear but not fully differentiated.

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 explicit workflow guidance: 'Review it with the user, then save accepted changes using the existing CRUD tools,' which tells the agent what this tool does NOT do and where to go next. It lacks an explicit 'when-not' or named alternative tool for edge cases, but the usage context is clear.

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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