feature-tracker-mcp

study_outcome

Record a study's outcome by linking it to a feature as spawned, killed, or informed, preserving the rationale for dropping features to prevent re-proposal without context.

Instructions

Record that a study spawned, killed or informed a feature. 'killed' matters as much as 'spawned': a study that closes work off is doing its job, and without the link the reason a feature was dropped disappears — leaving it to be re-proposed by someone who never saw the study.

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": {
      "study_id": "<study_id>",
      "feature_id": "<feature_id>",
      "relation": "<relation>"
    },
    "name": "study_outcome"
  }
}

Input Schema

Name	Required	Description
`cwd`	No	Directory identifying the project and the tree. Defaults to this session's working directory, which in a git worktree is that worktree.
`note`	No
`relation`	Yes
`study_id`	Yes
`feature_id`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 30, 2026

TDQS

B3.4/5.0

Behavior2/5

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

With no annotations, the description carries the full burden. It says 'Record' implying a write, and explains why recording killed matters, but it doesn't disclose whether repeated calls create duplicates, replace existing outcomes, require special permissions, have side effects on linked features, or what response to expect.

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?

Two sentences with front-loaded purpose. The second sentence earns its place by emphasizing the non-obvious importance of recording killed outcomes, so nothing is bloated or irrelevant.

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

Completeness2/5

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

For a write-style tool with no annotations and no output schema, important operational context is missing: idempotency, duplicate handling, whether a study can have multiple outcomes for the same feature, and what happens after recording. The rationale is well covered, but the invocation consequences are not.

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 coverage is only 20%, but the description gives meaning to the central relation values (spawned, killed, informed) and establishes which identifiers are involved. It doesn't elaborate on note or cwd beyond schema, and the exact nuance of 'informed' remains vague.

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 uses a specific verb ('Record') and a clear resource relationship: a study spawning, killing, or informing a feature. It differentiates from generic study lifecycle tools like study_conclude or study_list by naming the outcome relation, though it doesn't explicitly contrast it with a sibling like feature_link.

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 clear context for when the tool is appropriate: whenever a study has spawned, killed, or informed a feature, and it explicitly stresses that killed outcomes must be recorded as much as spawned ones. It doesn't name exclusions or alternative tools for other link types, but the usage context is concrete.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server