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evidence-readiness

io.github.BrienBounded/evr

by evidence-readiness
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check_trace_readiness

Check an execution trace for evidence readiness, reporting per requirement if evidence for post-hoc causal reconstruction is met, partially met, or unmet, naming missing fields and span kinds.

Instructions

Check an agent execution trace against the evidence-readiness specification and report, per requirement, whether the evidence a post-hoc causal reconstruction would need is met, partially met, or unmet — naming the specific missing field and the span kind it belongs on. Accepts a file/directory path OR inline JSON content (exactly one). Runs entirely locally in this process; nothing is transmitted or persisted, and inline content is processed in memory only. This tool NEVER determines whether a failure occurred: it answers "does this trace contain what a reconstruction of class X would require," never "did class X occur." A fabricated-but-internally-consistent trace passes. This tool verifies evidence is present, not that it is true, and its result must never be relayed as "this agent is safe".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
trace_pathNoPath to a trace .json file or a directory of traces (*manifest.json excluded).
require_classNoFold these classes' evidence requirements into the exit code (choices: config_drift, session_isolation_failure, cascade_contamination, prompt_injection).
trace_contentNoA trace document as a JSON string, checked in memory and never written to disk.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden, and it does so richly: local-only execution, nothing transmitted or persisted, inline content memory-only, and an explicit statement that it never determines whether a failure occurred. It also warns that a fabricated-but-consistent trace passes and that results must not be relayed as a safety verdict.

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

Conciseness4/5

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

Front-loaded with the core action, then inputs, then scope limits. Every sentence is substantive, though the final two sentences about fabricated traces and non-relay are dense with overlapping caveats that could be tightened.

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?

With no output schema, the description carries the return-value burden and does it: per-requirement status plus the specific missing field and its span kind. For a local, non-mutating analysis tool, an agent has everything needed to call it and interpret the result correctly.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds the meaningful mutual-exclusivity constraint ('exactly one' of path or inline content) that the schema does not encode. It also implies the reporting granularity tied to require_class, though it doesn't restate the enum values.

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 specific verb+resource (check a trace against the evidence-readiness specification) with a precise reporting scope: per-requirement met/partially met/unmet. It is clearly distinguishable from siblings get_requirement and list_class_requirements, which retrieve requirements rather than evaluate an artifact.

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?

Gives clear operating context: accepts a path OR inline JSON, 'exactly one', and runs locally in-process. It does not explicitly name the sibling tools or say when to call this versus fetching requirements first, leaving that routing to 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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