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recover_parameters

Recover real parameter values from auto_explain JSON logs to replace statistics synthesis, then use them to explain query plans or verify indexes.

Instructions

Recover REAL parameter values for a query from an auto_explain log — always a better grounding source than statistics synthesis. Requires auto_explain with log_format='json' and log_parameter_max_length != 0; pass the path to the (copied or mounted) log file.

Returns observed executions (slowest first) with their parameter values; feed those into explain_query or verify_index 'params'. Text values are redacted unless the profile opts out (redact = false).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sqlNo
profileYes
queryidNo
log_pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions returning 'observed executions (slowest first)', redacting text values unless 'redact = false', and requiring a copied/mounted log file. It does not discuss permissions or error handling, but covers key operational behaviors.

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?

The description is three sentences with the main purpose front-loaded. Each sentence adds distinct value: purpose, prerequisites, output behavior, and integration. There is no verbosity or redundancy.

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

Completeness3/5

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

The description covers prerequisites, output ordering, redaction, and downstream integration. However, with 0% schema coverage, missing explanations for 'sql' and 'queryid' create a clear gap in how to identify the query. The output schema exists, so return structure is handled, but parameter semantics remain incomplete.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate. It only clarifies 'log_path' ('pass the path to the (copied or mounted) log file') and hints at profile-based redaction. The roles of 'sql' and 'queryid' are never explained, leaving significant ambiguity in how to target a specific query.

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 'Recover REAL parameter values for a query from an auto_explain log', using a specific verb and resource. It distinguishes itself by claiming it is 'always a better grounding source than statistics synthesis' and by noting integration points with sibling tools like explain_query and verify_index.

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?

It provides explicit conditions for use: 'Requires auto_explain with log_format='json' and log_parameter_max_length != 0' and recommends feeding results into explain_query or verify_index. It positions itself as superior to statistics synthesis but does not explicitly state when not to use it, so it falls just short of a 5.

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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