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Prior attempts on an entity

prior_attempts
Read-onlyIdempotent

Check prior change attempts on an entity to avoid repeating rejected modifications. Use before editing to see if a similar change was reverted and adjust your plan accordingly.

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 plus the trail fields: outcome ("reverted" — a later removal on the path; "reopened" — reverted but a later supersede re-opened it; "active"), confidence ("proximity_high" / "proximity_low"), outcome_reasoning (WHY it was rejected), superseded_by + supersede_reasoning (the re-open, when present), and current_status — the entity's standing now. Treat "reverted" as "don't repeat this without a supersede"; "reopened" means the old revert no longer stands. Together they read: tried → reverted → re-opened. Templated and deterministic — no LLM call; pull-only.

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. Rows carry match_type ("exact" / "substring" / "fuzzy") and similarity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fuzzyNoOptional semantic query: also return attempts on entities whose prior reasoning is similar to this text — catches renames (payment_token vs card_token). Rows are labeled match_type='fuzzy' with a similarity score; without the selvedge[semantic] extra it falls back to substring matching and says so in a leading note row.
limitNoMaximum number of results.
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).
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`.
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).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Discloses that the tool is templated, deterministic (no LLM call), pull-only, and conservative by design. This adds significant value beyond the annotations (readOnlyHint, idempotentHint). No contradiction with 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 a brief definition, usage guidance, and detailed parameter explanations. Every sentence adds value, but it could be slightly more compact. Effectively front-loaded.

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 (6 params, deterministic, output schema), the description is comprehensive. It explains result fields (outcome, confidence, etc.), usage context, and parameter behaviors, leaving no gaps.

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?

All 6 parameters are described in the schema (100% coverage), and the description adds substantial context: fallback behavior for fuzzy, substring matching for description, precedence and examples for entity_path, confidence levels for min_confidence, and heuristic explanation for window_minutes.

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 it returns prior change attempts with inferred outcomes, which is a specific verb+resource. It distinguishes from siblings like 'history' or 'blame' by focusing on inferred outcomes and a trail of outcome fields.

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 advises calling before editing an entity, explains the purpose (avoid repeating rejected approaches), and gives guidance on when to adjust min_confidence. It provides clear context for when and why to use this tool.

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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