analyzing-releases

Analyzing Releases

Turn changelog data into competitive intelligence by analyzing release patterns across a cohort of related companies.

This is a reader workflow — it uses only the public, unauthenticated tools. There is no AI summarize/compare tool on the hosted MCP and no summary/compare command in the CLI; you fetch the raw release data and synthesize the analysis yourself.

Tools for each step

Operation	CLI (reader)	MCP tool
Find what's indexed for a company	releases search <company> --json	search (with type: ["orgs","catalog"])
List a company's sources	releases list --query <company> --json	list_catalog (scope with organization)
Latest releases (source or org)	releases tail <slug> --json · releases tail --org <org> --json	get_latest_releases (organization / product, since/until)
Keyword/semantic release search	releases search <query> --json	search with type: ["releases"]
Read one release in full	releases get <rel_id> --json	get_release
Read a tracked CHANGELOG slice	releases admin source changelog <slug> --tokens <n> (key-gated; readers use MCP)	get_catalog_entry with changelog_tokens / changelog_offset (keyless)

All releases are indexed already — you don't need to (and as a reader can't) trigger fetches. If a company isn't in the registry at all, say so rather than trying to onboard it; onboarding is an operator task.

Workflow

1. Define the cohort

Pick 3-6 companies in the same competitive space. Good cohorts share a common buyer or technical layer (e.g., developer databases, frontend frameworks, observability tools).

2. See what's indexed

Resolve each company with search (or releases search). For discovery-style cohorts ("find observability vendors with edge offerings"), search with type: ["orgs","catalog"] is vector-backed and matches on description and category — better than a slug-substring list_catalog --query. If a company isn't found, note it as a gap rather than fabricating data.

3. Pull latest releases with dates

Get structured release data per source or per org. Scope by organization for a whole-company view, or by a single source/product for a narrower one. Use a since window (e.g. "6m") or a count limit to bound the set. Dates are what you'll count for velocity, so keep them.

4. Search and cross-reference

Search across all indexed releases to find a specific feature, breaking change, or pattern. Release search is hybrid (lexical + semantic) by default, so natural-language queries like "auth refresh tokens" or "cold start improvements" work without exact keyword matches. Pass mode: "lexical" (CLI --mode lexical) when you need strict keyword behavior.

Result shape: every hit carries a kind discriminator:

• kind: "release" — a normal release row; use it directly.
• kind: "changelog_chunk" — a passage from a stored CHANGELOG.md. The hit includes the source id and the chunk's offset and length. To read the surrounding section before quoting, call get_catalog_entry with that source id and changelog_offset set to the chunk offset (add changelog_tokens, e.g. 2000, for a heading-aligned slice). Chunk hits often surface older or more granular notes than the indexed release rows, so they're useful for "when did X first ship" questions.

5. Synthesize

Combine the raw data into a structured analysis:

• Release velocity table — releases per company over the window, cadence pattern
• Trends adopted across the board — features 3+ companies shipped in the same window
• Differentiating bets — what each company is investing in that others aren't
• Gaps — what competitors shipped that a given company hasn't
• Forecasts — predictions grounded in pre-release tracks, deprecations, and trajectory

Output

Ask the user where to save the analysis, or use your best judgment based on the project's conventions. Include a short "Process Notes" section listing the exact commands/tools used, so the analysis is reproducible.

Important

• Focus on what companies shipped. If a source has noisy data (blog posts mixed in, missing dates), work around it silently — don't pad the report with source-quality commentary unless a company had to be substantially excluded.
• Fill data gaps with web fetches: list a source to get release URLs, then WebFetch to spot-check pages for a missing date, version, or detail.
• For velocity counting, lean on the dated rows from get_latest_releases / releases tail --json.
• Comparison and summarization are yours to synthesize from raw release data — there is no built-in tool that does it for you.