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check_feature_against_rules

Check a planned feature against a project's active rules and lessons, then return conflict context so AI agents can flag issues before implementation.

Instructions

Check a planned feature description against a project's active rules and lessons (detail="full"); persists a planning_checks record and returns the context for the calling LLM to flag conflicts.

Access level: analyze. Returns: JSON with feature_description, rules, lessons, check_id, and an instruction_for_client.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYesProject id whose active rules/lessons to check against.
feature_descriptionYesFree-text description of the feature being planned.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the write side effect (persisting a planning_checks record), the access level ('analyze'), and that the tool returns context for the calling LLM rather than a verdict. It does not cover idempotency or whether repeated checks duplicate records, but the key side effect is surfaced.

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?

Purpose is front-loaded in the first sentence, followed by compact labeled 'Access level' and 'Returns' lines. Efficient overall, though the stray '(detail="full")' aside is dead weight that does not correspond to any parameter.

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?

Both parameters are schema-covered and an output schema exists, yet the description still usefully names the returned JSON fields. The remaining gap is the undocumented/mismatched 'detail' reference, which slightly undermines completeness.

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 both parameters are fully documented in the schema and the baseline is 3. The description actually adds a reference to a 'detail="full"' parameter that does not exist in the schema, which is mildly misleading rather than helpful.

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 and resource: check a planned feature description against a project's active rules and lessons. It is clearly distinct from pure retrieval siblings like query_rules or get_rule_context. It does not explicitly name those siblings, so it stops short of 5.

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?

Usage is implied by 'planned feature description' and by the note that a planning_checks record is persisted before coding. However, no explicit when-to-use/when-not or routing against alternatives such as query_rules or get_rule_context is given.

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