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Delimit Ledger Query

delimit_ledger_query
Read-onlyIdempotent

Ask natural-language questions about the ledger and receive free-form answers. Maps plain queries to structured filters for read-only reporting.

Instructions

Ask natural-language questions about the ledger (ChatOps 2.0).

When to use: when an operator wants a free-form answer ("what shipped this week?", "what's blocked?", "show all P0s") rather than a structured filter query. When NOT to use: for structured listing (use delimit_ledger_list) or top-N summary (delimit_ledger_context).

Sibling contrast: delimit_ledger_list takes structured filters; this maps natural language to those filters internally.

Side effects: read-only. Internally calls list / context queries.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural-language question (e.g. "what's blocked?", "search for dashboard"). Required.
ventureNoProject name/path. Empty = auto-detect.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changedv4.7.9
    • changedInput schema / properties / query / description
      Previous value: -"Natural language question about the ledger."New value: +"Natural-language question (e.g. \"what's blocked?\", \"search for dashboard\"). Required."
    • changedInput schema / properties / venture / description
      Previous value: -"Project name or path. Auto-detects if empty."New value: +"Project name/path. Empty = auto-detect."
  2. Addedv4.5.5

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description reinforces this by stating 'Side effects: read-only.' It adds useful internal behavior context by noting that it maps natural language to filters and internally calls list/context queries, which is value beyond the annotations.

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 well-structured with clear headings, front-loads the core purpose, and includes only high-value details: examples, exclusions, sibling contrast, and side effects. Every sentence earns its place.

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 simple 2-parameter schema with full coverage, an output schema, and thorough annotations, the description fully covers what an agent needs: when to use it, when not to, how it differs from siblings, and its side effects. Nothing essential is missing.

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%, so the schema already documents both parameters. The description adds helpful examples of natural-language queries but does not add significant meaning to the 'venture' parameter beyond what the schema provides. Baseline 3 is appropriate.

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's purpose: asking natural-language questions about the ledger, with concrete examples. It also explicitly distinguishes this tool from delimit_ledger_list and delimit_ledger_context, making the resource and behavior unambiguous.

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 provides explicit 'When to use' and 'When NOT to use' sections, naming the specific alternative tools for structured listing and top-N summaries. This gives an agent clear routing criteria without requiring inference.

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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