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Glama

Append evidence

evidence

Record completed work by appending a proof line to a work item's evidence field, making it readable by gates and distillers. Use this to document done-work proof.

Instructions

Append one evidence line to a work item's evidence field: the canonical way to record done-work proof, which dor, gate and distill read. Success is silent. Equivalent to field with field=evidence and op=add, but self-documenting.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYeswork item id W-NN
textYesevidence line to append

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.3/5.0
Behavior4/5

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

Adds 'Success is silent,' a useful behavioral trait not present in the annotations and especially relevant given the absence of an output schema. It also notes that dor, gate, and distill read the evidence, providing downstream context, though it does not cover auth, failure modes, or reversibility.

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 short sentences with no filler: the action, canonical status, downstream readers, silent success, and equivalence to field are all communicated efficiently. The key information is front-loaded and every clause 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?

For a simple two-parameter append operation, the description is complete: it defines the purpose, the canonical use case, downstream consumers, and the silent success behavior. No critical invocation detail 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?

The input schema already fully describes both parameters, so the description adds little parameter-specific meaning. It reinforces that text is an evidence line, but this is largely a restatement of the schema description.

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?

States a precise action: appending one evidence line to a work item's evidence field. It also positions itself as the canonical way to record done-work proof and explicitly compares to field with op=add, which separates it from generic field manipulation.

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?

Describes the tool as 'the canonical way' to record evidence and explains the equivalence to field with field=evidence and op=add, giving clear context for when it should be chosen. It does not explicitly state when to avoid it, but the 'canonical' framing and comparison make the intended usage clear.

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