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Prior attempts on an entity

prior_attempts
Read-onlyIdempotent

Retrieve prior change attempts on an entity to avoid repeating rejected edits. Shows inferred outcomes (reverted or active) and reasons.

Instructions

Prior change attempts on an entity, each with an inferred outcome.

Call this BEFORE editing an entity. If the same change was tried before and reverted, you get the prior reasoning and change_type plus an inferred outcome — so you can change your plan instead of repeating a rejected approach.

Each result is a change event with three extra fields:

  • outcome — "reverted" (a later remove/delete on the same path) or "active" (no later removal seen).

  • confidence — "proximity_high" or "proximity_low".

  • outcome_reasoning — the reverting event's reasoning (WHY it was rejected), or "" while still active.

Outcome is inferred from add->remove proximity — v0.3.7 has no explicit reject/revert change types yet (those arrive in v0.3.11). Output is templated, deterministic, and makes no LLM call. This is a pull-only tool: it never writes and never pushes; you decide when to ask.

Conservative by design — min_confidence defaults to "proximity_high", so an empty list (nothing clearly tried-and-rejected) is the normal, preferred answer over a speculative false positive. Pass min_confidence="proximity_low" to widen recall.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entity_pathNoThe entity you're about to change. Exact path with prefix matching — 'users' also covers 'users.email'. Examples: 'src/auth.py::login', 'users.email', 'env/STRIPE_SECRET_KEY'. Provide this OR `description`.
descriptionNoFree-text description of what you're about to do, when you don't have an exact entity_path. Matched as a substring against prior reasoning, diffs, and entity paths. Provide this OR `entity_path` (entity_path takes precedence if both are given).
min_confidenceNoConfidence floor. 'proximity_high' (default) returns only attempts that were clearly tried and then reverted within the window — the high-signal 'rejected before' cases. Pass 'proximity_low' to also see the noisy tail (still-active changes and far-apart reverts).proximity_high
window_minutesNoProximity window in minutes for the add->remove revert heuristic. An attempt removed within this many minutes is 'proximity_high'; beyond it, 'proximity_low'. Default 10080 (7 days).
limitNoMaximum number of results.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

The description goes well beyond annotations by detailing the outcome inference mechanism (proximity-based), the conservative default (`min_confidence='proximity_high'`), and the deterministic, read-only nature. It explicitly states the tool 'never writes and never pushes' and makes no LLM call, which matches the `readOnlyHint` and `idempotentHint` 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?

The description is well-structured: it starts with a concise summary, followed by usage guidance, then detailed output field explanations, and ends with parameter defaults. While somewhat lengthy, every sentence adds value, and the structure is logical.

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 the tool's complexity (5 parameters, output schema, annotations), the description covers all essential aspects: what the tool does, when to use it, how inference works, parameter meanings, and conservative behavior. No gaps are apparent.

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?

All 5 parameters have schema descriptions (100% coverage), and the description adds significant value: it explains the mutual exclusivity of `entity_path` and `description`, the confidence levels, the window_minutes heuristic, and the limit. This extra context aids correct usage beyond the schema alone.

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 clearly states the tool's purpose: retrieving prior change attempts on an entity with inferred outcomes. It specifies the resource ('prior attempts') and the action ('get'), and distinguishes itself from siblings like 'history' or 'diff' by focusing on outcome inference.

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?

The description explicitly advises calling this tool 'BEFORE editing an entity' and explains why (to avoid repeating rejected approaches). It also provides guidance on the `min_confidence` parameter. While it doesn't directly contrast with sibling tools, the usage context is clear and actionable.

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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