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Number Parity (summary vs body)

lens_consistency_numbers

Verify every numeric literal in a summary appears in the source body, catching invented figures. Deterministic literal matching flags inconsistencies; review each hit rather than auto-trusting.

Instructions

Check that every numeric literal in a summary actually appears in the body it summarizes. Deterministic, no LLM, exit 6 on a violation. Catches the invented figure — the number a summary asserts that its source never stated. TRIPWIRE, not an oracle: literal matching only, no semantic or derived arithmetic, so a legitimately computed total will flag. Review a hit, do not auto-trust it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cwdNoWorking directory to run in. catches.jsonl is read from and written to here. Defaults to the server's cwd.
bodyYesThe body file the summary summarizes
summaryYesThe summary file (its numbers must appear in the body)

Schema Changelog

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

  1. First observedv1.1.1

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations, the description carries the full behavioral burden, and it delivers: it discloses deterministic matching, no LLM usage, exit code 6 on violation, literal-only matching with no semantic or derived arithmetic, and the caveat that legitimately computed totals will flag. This gives the agent an unusually complete picture of the tool's behavior and limitations.

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 compact and front-loaded, starting with the core check, then adding determinism, exit behavior, purpose, limitation, and a review caution. Every sentence adds distinct information, and there is no filler.

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?

The description is complete for a tool with no annotations and no output schema: it covers what is checked, how it behaves, what exit code signals a violation, what its limitations are, and how the agent should handle results. The schema covers parameter details and the catches.jsonl side effect, so nothing critical 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 the summary, body, and cwd parameters well. The tool description reinforces the relationship between summary and body numbers but does not add parameter-level detail beyond what the schema provides.

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 names a specific verb and resource: check that every numeric literal in a summary appears in the body it summarizes. It clearly distinguishes the tool from siblings by focusing on numeric literal parity with deterministic behavior and exit 6 on violation.

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?

The description provides clear operational context: it is deterministic, uses no LLM, and is a tripwire rather than an oracle. It explains when the result may be a false positive and tells the agent to review hits, though it does not explicitly compare against sibling tools or state when not to use it.

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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