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Prior attempts on an entity

prior_attempts

Read-onlyIdempotent

Check past change attempts and inferred outcomes before editing an entity, so you can avoid repeating reverted or rejected approaches.

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" — closed but a later supersede re-opened it; "rejected" — a standalone reject event that closed no earlier attempt, surfaced as its own row whose reasoning IS the record; "active"), confidence ("exact" — the attempt was closed by an explicit revert/reject, or the row is a standalone rejection; "proximity_high" / "proximity_low" — the add->remove window heuristic for implicit removals), 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" and "rejected" as "don't repeat this without a supersede"; "reopened" means the old verdict 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; "exact" rows always clear that default floor. Pass min_confidence="proximity_low" to widen recall. Rows carry match_type ("exact" / "substring" / "fuzzy") and similarity.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`fuzzy`	No	Optional 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.
`limit`	No	Maximum number of results.
`description`	No	Free-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_path`	No	The 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_confidence`	No	Confidence floor. 'proximity_high' (default) returns the high-signal rows: attempts closed by an explicit revert/reject (confidence 'exact' — always clears this floor, including standalone rejections) plus attempts reverted within the window. Pass 'proximity_low' to also see the noisy tail (still-active changes and far-apart reverts).	proximity_high
`window_minutes`	No	Proximity window in minutes for the add->remove revert heuristic — the tiebreaker for IMPLICIT removal types only. An attempt removed within this many minutes is 'proximity_high'; beyond it, 'proximity_low'. Attempts closed by an explicit revert/reject are 'exact' regardless of the window. Default 10080 (7 days).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv0.3.14Sep 19, 2026
- changedInput schema / properties / window_minutes / maximum
```
Previous value: -1000New value: +10080
```

Changed2 schema fields changedSep 1, 2026

changedInput schema / properties / min_confidence / description

Previous value: -"Confidence 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)."New value: +"Confidence floor. 'proximity_high' (default) returns the high-signal rows: attempts closed by an explicit revert/reject (confidence 'exact' — always clears this floor, including standalone rejections) plus attempts reverted within the window. Pass 'proximity_low' to also see the noisy tail (still-active changes and far-apart reverts)."

changedInput schema / properties / window_minutes / description

Previous value: -"Proximity 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)."New value: +"Proximity window in minutes for the add->remove revert heuristic — the tiebreaker for IMPLICIT removal types only. An attempt removed within this many minutes is 'proximity_high'; beyond it, 'proximity_low'. Attempts closed by an explicit revert/reject are 'exact' regardless of the window. Default 10080 (7 days)."

Changed2 schema fields changedv0.3.11Aug 2, 2026
- addedInput schema / properties / limit / maximum
```
Added value: +1000
```
- addedInput schema / properties / window_minutes / maximum
```
Added value: +1000
```

Changed1 schema field changedv0.3.10Jul 13, 2026

addedInput schema / properties / fuzzy

Added value: +{
+  "default": "",
+  "description": "Optional 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.",
+  "title": "Fuzzy",
+  "type": "string"
+}

Addedv0.3.8Jun 17, 2026

TDQS

A4.3/5.0

Behavior5/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior, and the description reinforces and expands this with 'Templated and deterministic — no LLM call; pull-only.' It discloses nuanced behaviors: conservative defaults, the meaning of outcome/confidence values, and that an empty list is the preferred normal answer. 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?

The description is long but information-dense, with a sensible structure: core purpose, usage timing, outcome semantics, and confidence policy. Every sentence carries meaningful guidance, though some sections could be tightened. The front-loading is effective; the most important instruction appears early.

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, the description covers purpose, usage timing, result semantics, confidence filtering, recall widening, and edge cases like standalone rejections and reopen events. The output schema exists and the description also explains return fields thoroughly. Nothing critical is missing for an agent to select and invoke this tool correctly.

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 coverage is 100%, so the schema itself documents all parameters. The description adds meaningful extra context, such as the default min_confidence behavior, how 'exact' rows clear the confidence floor, and the role of window_minutes as a tiebreaker for implicit removals. This goes beyond simple schema repetition, though it could have been slightly more parameter-by-parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool's purpose: retrieving prior change attempts on an entity with inferred outcomes. It states a specific action context ('Call this BEFORE editing an entity') and distinguishes the data it returns. However, it does not explicitly differentiate itself from siblings like 'history' or 'changeset', so an agent must infer which tool covers which kind of history.

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?

Explicitly instructs when to use the tool: before editing an entity, to avoid repeating a rejected approach. It also explains how to widen recall via min_confidence. However, it does not say when NOT to use it or name any alternative tool, so the usage guidance is strong on 'when' but missing exclusions and alternatives.

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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