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Jason Lovell

by ai.jasonlovell
OverviewDiscussionsAdmin

Read a study

get_study
Read-onlyIdempotent

One study in full: the question, the result, the design decision, what was recorded, the evidence links and where the evidence stops.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "slug": "<slug>"
    },
    "name": "get_study"
  }
}

Input Schema

NameRequiredDescriptionDefault
slugYesThe study's slug, from list_studies.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / slug / enum
      Previous value: -[
-  "mcp-continuation-replay",
-  "scaffold-arena",
-  "realitybridge",
-  "shadowskillbench",
-  "living-context",
-  "jev-decision-lab",
-  "ghosttrace",
-  "no-model-floor",
-  "cascadeshift",
-  "nano-interpretability"
-]New value: +[
+  "tool-contract-drift",
+  "reasoning-under-change",
+  "executable-clarifications",
+  "mcp-continuation-replay",
+  "scaffold-arena",
+  "realitybridge",
+  "shadowskillbench",
+  "living-context",
+  "jev-decision-lab",
+  "ghosttrace",
+  "no-model-floor",
+  "cascadeshift",
+  "nano-interpretability"
+]
  2. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already establish that this is a safe, idempotent read. The description adds useful behavioral context by explaining the full contents returned, including evidence links and where the evidence stops, though it does not cover error behavior for invalid slugs.

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 a single front-loaded sentence with a colon-delimited list of returned content. It is efficient and every phrase contributes meaning.

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 read-one tool with a fully described single parameter and clear annotations, the description is complete. It compensates for the absence of an output schema by summarizing the study fields returned.

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 single slug parameter is fully documented in the schema, including its enum and note that it comes from list_studies. The description itself does not add further parameter semantics, which is acceptable given 100% schema coverage.

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 clearly states that this retrieves a single study in full and enumerates the sections returned. It implies a read-one operation in contrast to list_studies, but it does not explicitly name or differentiate itself from sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no explicit guidance on when to use this tool versus alternatives such as list_studies or search_site. Usage is only implied by the singular 'one study' framing and the required slug.

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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