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get_graph_timeline

Read-onlyIdempotent

Sample historical commits at even intervals to produce a timeline of file counts, commit churn, and current graph metrics per period for code evolution analysis.

Instructions

SIMPLIFIED first version of a continuous graph-evolution timeline: samples evenly-spaced historical commits across the requested window (via git log, same sampling strategy as get_complexity_trend) and reports file-count + commit churn (files changed/insertions/deletions) per period, with a short narrative diff marker (e.g. "+12 files, 8 commit(s), +540/-120 lines"). Symbol/edge counts are reported for the current HEAD snapshot only — NOT reconstructed per historical commit (that would require re-indexing the full tree at every sampled commit, which this tool deliberately avoids). For point-in-time named checkpoints use snapshot_graph + diff_graph_snapshots instead. Requires git. Read-only. Returns JSON: { since_days, granularity, periods: [{ period, commit, date, file_count, commits_in_period, files_changed, insertions, deletions, narrative }], current: { files, symbols, edges_by_type }, _tier, _methodology }.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
since_daysNoGit history window in days (default: 90)
granularityNoBucket size for periods (default: monthly)
max_periodsNoCap on number of periods returned, most recent kept (default: 24)
Behavior5/5

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

Discloses that symbol/edge counts are for current HEAD only, not reconstructed per commit, and that it uses the same sampling strategy as get_complexity_trend. Annotations already provide readOnlyHint and idempotentHint, but description adds concrete behavioral details beyond annotations.

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?

Well-structured with front-loaded purpose, then details, exclusions, requirements, and output format. Slightly verbose but every sentence is informative.

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?

Given no output schema, the description fully explains the return JSON structure. Covers prerequisites, limitations, and provides complete context for usage.

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 covers all parameters with descriptions. Description adds context by explaining sampling strategy and output format, but doesn't significantly extend parameter semantics beyond schema. Since schema coverage is 100%, baseline is 3, but description adds some value.

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?

Description clearly states it provides a continuous graph-evolution timeline by sampling historical commits and reporting per-period file-count and commit churn. It uses specific verbs like 'samples' and 'reports', and distinguishes itself from snapshot_graph + diff_graph_snapshots.

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?

Explicitly states when to use this tool vs alternatives: 'For point-in-time named checkpoints use snapshot_graph + diff_graph_snapshots instead.' Also notes requires git and is read-only.

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

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