Skip to main content
Glama

sumo_qa_analyze_diff_impact

Maps changed files to their likely tests, identifies risk surface (uncovered changes), one-hop affected nodes, and unmapped files from a diff against a base ref.

Instructions

Map a set of changed files onto the repo-map to report which tests likely exercise them, which changed sources have no mapped test (the risk surface), one-hop affected nodes, unmapped files, and whether the map is stale relative to HEAD.

Common natural-language phrasings that map to this tool: "what does this diff affect", "which tests cover my changes", "what's the risk surface of this branch", "what should I re-test after these edits", "analyse the impact of the changes against main".

root is the repository. Supply changed_files (repo-relative paths) OR base_ref (any git ref; changed files are the diff against the merge-base of base_ref and HEAD, so changes that landed on the base after the branch diverged don't leak in). The repo-map is read from artifact_path when present and falls back to a live scan otherwise; an artifact for a different project root is ignored. On the first run of an unmapped repo the live scan is persisted to artifact_path (reported as persisted_map_path) unless artifact_path is None. write_overlay writes .sumo-qa/diff-impact.json under root. When test files exist but the map has no likely_tests edges, probable_mapping_gap flags the risk surface as a missed-convention gap rather than true zero coverage.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rootYes
base_refNo
artifact_pathNo.sumo-qa/repo-map.json
changed_filesNo
write_overlayNo
Behavior5/5

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

Discloses key behavioral traits: writes overlay file, persists map on first run, handles stale maps, and mutual exclusivity of inputs. Annotations are minimal (readOnlyHint=false, etc.), so the description adds significant value beyond them. No contradiction.

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?

Description is relatively long but well-structured: purpose first, then phrasings, then parameter details. Every sentence adds value. Could be slightly more concise, but front-loaded with core output and efficiently organized.

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?

Despite no output schema, describes the output components (likely tests, risk surface, etc.) and edge cases (stale map, first-run, probable_mapping_gap). Provides a complete picture of what the tool does and returns, sufficient for an AI agent to understand usage and outcomes.

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

Parameters5/5

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

Input schema has 0% description coverage, but the tool description thoroughly explains each parameter: root, changed_files, base_ref, artifact_path, write_overlay. It clarifies defaults, mutual exclusivity, and behavior (e.g., artifact path fallback, first-run persistence). This fully compensates for missing schema descriptions.

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?

Clearly states the tool maps changed files to tests and reports risk surface, likely tests, etc. Uses specific verbs and resources, and lists common user phrasings, distinguishing it from sibling tools like 'sumo_qa_query_repo_map'.

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?

Provides clear context on when to use (analyze diff impact, find test coverage). Explains the two input modes (changed_files or base_ref) and relationship. Does not explicitly mention when not to use or contrast with alternatives, but the natural language mappings serve as good usage cues.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/sumithr/sumo-qa'

If you have feedback or need assistance with the MCP directory API, please join our Discord server