Skip to main content
Glama

Prior attempts on an entity

prior_attempts
Read-onlyIdempotent

Review an entity's previous change attempts before editing to avoid repeating rejected work. Each attempt includes reasoning, change type, and inferred outcome (reverted, reopened, active) for informed planning.

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?

Annotations already mark the tool as read-only and idempotent; the description adds that it is templated, deterministic, pull-only, and conservative by design, warning that an empty list is the preferred answer over speculative false positives. It also discloses fallback substring matching behavior and that a leading note row indicates this fallback.

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 well-structured: summary, usage context, output field interpretation, and tuning parameters. The output field explanation is dense but earns its place by explaining the meaning of 'reverted' and 'reopened'. It is slightly verbose but appropriate for the tool's complexity.

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?

With an output schema present, the description need not restate return values, but it explains the conceptual model (outcome trail, confidence, supersede, current_status) essential for correct interpretation. It gives the agent complete context for when and how to use the tool, including edge cases like empty results.

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?

Even with 100% schema coverage, the description adds crucial semantics: the precedence rule between entity_path and description, the fuzzy matching fallback when the semantic extra is unavailable, and the interpretation of min_confidence values and window_minutes. This goes well beyond the schema's field descriptions.

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 returns prior change attempts with inferred outcomes, and the imperative 'Call this BEFORE editing an entity' makes the action and resource explicit. It is distinct from sibling tools like history or diff by focusing on rejected/reopened attempts with outcome reasoning.

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?

The description gives a strong directive to call before editing and explains how the outcome field can prevent repeating a rejected approach. It also provides explicit tuning guidance via min_confidence and explains when to prefer the default 'proximity_high' versus widening recall with 'proximity_low'.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/masondelan/selvedge'

If you have feedback or need assistance with the MCP directory API, please join our Discord server