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Selvedge

by masondelan
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Python
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Search events

search
Read-onlyIdempotent

Search across entity paths, diffs, reasoning, and agent fields to locate code changes, added columns, or feature-related decisions.

Instructions

Full-text search across entity paths, diffs, reasoning, and agents.

Useful for questions like 'what changes were made for the billing feature?', 'which columns were added by cursor?', or 'show everything related to authentication'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results.
queryYesSearch string (case-insensitive substring). Searches across entity_path, diff, reasoning, and agent fields. SQL LIKE wildcards (`_` and `%`) are escaped, so 'stripe_customer_id' matches the literal underscore rather than any single char.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.3.11
    • addedInput schema / properties / limit / maximum
      Added value: +1000
  2. Changed3 schema fields changedv0.3.2
    • addedInput schema / properties / limit / description
      Added value: +"Maximum number of results."
    • addedInput schema / properties / limit / minimum
      Added value: +1
    • addedInput schema / properties / query / description
      Added value: +"Search string (case-insensitive substring). Searches across entity_path, diff, reasoning, and agent fields. SQL LIKE wildcards (`_` and `%`) are escaped, so 'stripe_customer_id' matches the literal underscore rather than any single char."
  3. First observedv0.3.1

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds search semantics: full-text across four fields and substring matching with escaped wildcards (from schema). This contextualizes behavior beyond annotations, though pagination/ordering are not mentioned (but output schema covers returns).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: one declarative purpose statement and one illustrative set of examples. No redundant content and the main intent is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a search tool with two parameters, full schema coverage, output schema, and clear annotations, the description adequately conveys what it searches. The example questions help the agent map natural language to tool invocation; however, it does not mention result ordering or the limit parameter behavior (though schema covers limit).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, including the case-insensitive substring behavior and wildcard escaping. The tool description adds only usage examples, not new parameter semantics, so it meets baseline but does not exceed schema detail.

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 uses the specific verb 'search' and names the exact resources (entity paths, diffs, reasoning, agents). Example queries like 'what changes were made for the billing feature?' clarify the scope and distinguish it from sibling tools like diff or blame.

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?

Provides explicit example questions that signal appropriate use cases, such as cross-cutting search across multiple entities. It does not directly name alternatives or state when not to use this tool, but the examples imply broad search rather than targeted diffs or history queries.

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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