django-chainsaw-mcp

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check

Read-onlyIdempotent

Run all Django project analyses in one call and receive a combined, severity-sorted list of risks. Eliminates the need to choose among multiple specialized tools.

Instructions

Run every analysis and return one severity-sorted list.

The single call to reach for on an unfamiliar project. It runs the checks
whose findings are defects, merges them, and sorts by severity, instead of
making you know which of a dozen tools to ask for.

A check that fails to run is listed in `checks_failed` rather than counted
as clean.

This is the one slow call here - a minute or more on a large project - and
it reports progress as each check starts, so a client can name the check
that is running instead of showing nothing for a minute.

Args:
    tenant_root: the model that owns data, for the ownership check.
    only: run just these checks.
    skip: run everything except these.

Input Schema

TableJSON Schema

Name	Required	Default
`only`	No
`skip`	No
`tenant_root`	No	auth.User

Output Schema

TableJSON Schema

Name	Required	Description
`ok`	No	present and false only when the run could not happen at all
`note`	No
`error`	No
`findings`	No
`checks_run`	No	per check: whether it ran, and how many it produced
`frameworks`	No	what was detected in the project, and its version
`by_severity`	No
`checks_failed`	No	checks that raised. These are not clean results - a project with a failed check has fewer findings than it has problems.
`finding_count`	No
`checks_not_applicable`	No	check name to the reason it does not apply here

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.3Sep 9, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Beyond the readOnly/idempotent/destructive annotations, the description adds important behaviors: failed checks appear in 'checks_failed' rather than being treated as clean, this is 'the one slow call here,' and it 'reports progress as each check starts.' These details materially change how a client should call and present the tool.

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?

The description is compact and front-loaded: purpose first, use case second, distinctive failure/perf behavior third, and parameter meanings last. No sentence is wasted; each adds either routing, behavioral, or parameter value.

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

Completeness5/5

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

For an aggregate tool with three optional parameters and an output schema, the description covers what the tool does, when to use it, how failures are represented, performance expectations, progress reporting, and all parameters. The existence of an output schema excuses it from detailing return structure.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description carries the burden, and it does: 'tenant_root: the model that owns data, for the ownership check,' 'only: run just these checks,' and 'skip: run everything except these.' It gives each parameter purpose beyond the raw names, though it could be more explicit that only/skip expect sibling check names.

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?

The description opens with a specific verb and resource: 'Run every analysis and return one severity-sorted list.' It explicitly frames the tool as the aggregate entry point that 'runs the checks whose findings are defects, merges them, and sorts by severity,' which distinguishes it from the many sibling analysis tools without requiring the agent to know which one to pick.

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 a clear use context: 'The single call to reach for on an unfamiliar project' and says it exists 'instead of making you know which of a dozen tools to ask for.' It does not explicitly enumerate when-not-to-use or name a specific alternative, but the guidance is strong enough for an agent to select it as the broad entry point.

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