Execution Evidence Lab

by site.chatgpt.tuned-drake-1114.execution-evidence-lab

Find measured Python previews and scoped research links

find_evidence

Read-onlyIdempotent

Use when debugging a public Python error and looking for an existing measured reproduction. Search by exact error or library name alone (library browsing). Longer problems need a measured-topic clue; a lone library mention does not imply a matching fix. Curated examples: asyncio, gather, TaskGroup, cancellation, ExceptionGroup, numpy.dtype size changed, binary incompatibility, NumPy 2, pandas, PyArrow, pydantic partial update, pydantic PATCH, exclude_unset, exclude_none, model_fields_set, pydantic-settings, pydantic, extra_forbidden, Extra inputs are not permitted, dotenv, SQLAlchemy, AsyncSession, AsyncAttrs, MissingGreenlet, greenlet_spawn has not been called, sqlite, sqlalchemy, no such table, in memory, StaticPool, sqlite3, savepoint, rollback, release, httpx, starlette, TestClient, unexpected keyword argument app, lifespan, subprocess, Popen, PIPE, communicate, TimeoutExpired, urllib.parse, urljoin, same origin, URL prefix, userinfo, datetime, zoneinfo, DST, elapsed time, fold. Returns ranked previews with record_id, test_id, scope, platform, page_url and separately scoped supplementary_evidence when available; no primary match returns an empty records array. Separately typed research_supplements may provide existing non-Python research files, e.g. MCP cancellation/retry accounting in SDK v1.30.0; inspect their version limits and file URLs, not read_evidence. A supplement is not a primary record or upstream resolution. Read the preview before choosing read_evidence for receipt-free retrieval; get_evidence additionally creates an optional receipt and private report proof. Full records and files are freely readable. Not a general web search or proof of compatibility. Requests are logged.

Input Schema

TableJSON Schema

Name	Required	Description
`query`	No	Public error or library, e.g. numpy.dtype size changed or TestClient. Omit to browse all previews. Do not send private tracebacks.
`test_run`	No	Set for synthetic, developer, or invited tests so they are excluded from external-use candidates.
`agent_name`	No	Optional client name. Self-declared, never proof of AI identity.
`discovery_source`	No	Optional: search, official-registry, direct, or other source. Self-reported.

Output Schema

TableJSON Schema

Name	Required	Description
`records`	Yes
`next_action`	Yes
`match_status`	Yes
`primary_record_count`	No
`research_supplements`	No	Separate research artifacts with their own environment and scope; not primary Python records. Read returned file URLs rather than passing their id to read_evidence.
`research_match_status`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedOct 2, 2026

addedOutput schema / properties / primary_record_count

Added value: +{
+  "minimum": 0,
+  "type": "integer"
+}

addedOutput schema / properties / research_match_status

Added value: +{
+  "enum": [
+    "scoped_supplements",
+    "no_results"
+  ]
+}

addedOutput schema / properties / research_supplements

Added value: +{
+  "description": "Separate research artifacts with their own environment and scope; not primary Python records. Read returned file URLs rather than passing their id to read_evidence.",
+  "items": {
+    "additionalProperties": true,
+    "type": "object"
+  },
+  "type": "array"
+}

Changed1 schema field changedOct 2, 2026
- addedOutput schema / properties / records / items / properties / reproduction_advisories
```
Added value: +{
+  "items": {
+    "additionalProperties": true,
+    "type": "object"
+  },
+  "type": "array"
+}
```
Changed1 schema field changedOct 2, 2026
- addedOutput schema / properties / records / items / properties / supplementary_evidence
```
Added value: +{
+  "items": {
+    "additionalProperties": true,
+    "type": "object"
+  },
+  "type": "array"
+}
```
AddedOct 1, 2026

TDQS

A3.6/5.0

Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, so the bar is lower, yet the description adds substantial behavioral context: empty-result behavior ('no primary match returns an empty records array'), that requests are logged, that records and files are freely readable, and that research_supplements are a separate, non-primary type with version limits to inspect. These are traits the annotations do not convey.

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

Conciseness2/5

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

The tool's key rule is front-loaded, which is good, but roughly two-thirds of the description is an unstructured comma-dump of dozens of library and error keywords (asyncio, numpy.dtype size changed, greenlet_spawn has not been called, etc.). This bloats the definition, dilutes the behavioral statements, and does not read as curated guidance an agent can act on.

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

Completeness4/5

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

For a read-only search tool with full schema coverage and an output schema, the description is close to complete: it covers triggering conditions, query construction, result shape at a high level, and the distinction from read_evidence/get_evidence and research_supplements. The only shortfall is that the keyword dump substitutes for a clearer statement of what 'measured topic clue' means.

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 all four parameters, making 3 the baseline. The description adds a little meaning ('search by exact error or library name alone', 'do not send private tracebacks', omission browses all previews), but much of that is duplicated from the schema text. Net value over structured fields is modest.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb and resource: search for existing measured Python reproductions by exact error or library name, and it explicitly bounds scope ('Not a general web search or proof of compatibility'). It also differentiates itself from the sibling read_evidence and the related get_evidence, so an agent can distinguish it from neighbors. The signal is clear despite being buried under a long keyword list.

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 opens with an explicit trigger ('Use when debugging a public Python error and looking for an existing measured reproduction') and adds a real usage rule ('Longer problems need a measured-topic clue; a lone library mention does not imply a matching fix'). It names the downstream alternative (read_evidence for receipt-free retrieval, get_evidence for a receipt) and states when not to use it (general web search, compatibility proof). No explicit when-not for other siblings like read_thread, but coverage is strong.

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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