Skip to main content
Glama
The Intelligence Radar MCP server

The Intelligence Radar MCP server

Official
by BrightbeamAI

Revise a contribution

revise_contribution

Submit a revised contribution draft when an item is in review or reviewers request changes; this updates the GitHub issue and invalidates earlier approvals.

Instructions

Send a revised version of your contribution while it is in review or after the reviewers asked for changes. First prepare the revision with prepare_contribution (the same kind), show the person the preview and get their explicit agreement; then call this with the contribution's id and the new draft_id. It updates the GitHub issue; approvals of earlier versions no longer count.

Input Schema

NameRequiredDescriptionDefault
idYesThe contribution: its id, such as C-0007, or its GitHub issue number, such as #7
draft_idYesThe draft_id that prepare_contribution returned in this conversation

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2026.2.0

TDQS

A4.7/5.0
Behavior5/5

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

Annotations only declare the generic write profile (readOnlyHint=false, destructiveHint=false, openWorldHint=true, idempotentHint=false). The description adds two non-obvious consequences: the GitHub issue is updated, and approvals of earlier versions no longer count — a state effect an agent could not infer from structured fields. It also imposes a human-in-the-loop confirmation requirement before invocation.

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?

Three sentences, front-loaded with the state condition, then the prerequisite workflow, then the side effect. No filler, and every clause carries operational meaning for the caller.

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 a two-parameter mutation with no output schema, the definition covers the trigger state, the mandatory preparation path, the argument sourcing, and a consequential side effect. Nothing an agent needs in order to call it safely is missing.

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 already document their expected values, including that draft_id comes from prepare_contribution in this conversation. The description restates this pairing but adds no format or edge-case detail beyond the schema, so the baseline 3 applies.

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?

Uses a specific verb and resource ('Send a revised version of your contribution') and scopes it to a precise state ('while it is in review or after the reviewers asked for changes'). The reader can distinguish it from submit_contribution (new work) and prepare_contribution (preparation step) without opening any schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

States the trigger conditions explicitly and spells out the prerequisite sequence: prepare with prepare_contribution, show the preview, get explicit agreement, then call this. The alternative (prepare_contribution) is named along with the condition that selects it.

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