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io.github.loopg/pramana-mcp

io.github.loopg/pramana-mcp

by reliai-in
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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get_model_diff

Retrieve one comparison run's findings: see which decisions changed behaviorally, which were only cosmetic, and which runs halted.

Instructions

Get the findings of one comparison run: which decisions changed behaviourally, which were cosmetic, and which runs halted.

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": {
      "diff_id": "<diff_id>"
    },
    "name": "get_model_diff"
  }
}

Input Schema

NameRequiredDescriptionDefault
diff_idYes
trace_idNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.3

TDQS

B3/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden. It does disclose the substantive content of the result (behavioural vs cosmetic changes, halted runs), which is real signal beyond the schema. However, it says nothing about read-only nature, error behaviour for an unknown diff_id, or what the optional trace_id scopes.

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?

A single front-loaded sentence with a compact colon list; no filler. It is slightly dense at the tail but every clause adds information about the return content.

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

Completeness3/5

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

An output schema exists, so return structure need not be restated, and the description adequately previews the result categories. The substantial gap is parameter handling: both diff_id and trace_id are undocumented in schema and description alike, which is not adequate for a 2-param tool with 0% coverage.

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

Parameters2/5

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

Schema description coverage is 0% and the description never references either parameter. It does not explain what a diff_id is or how to obtain one, nor what the optional trace_id does (filter? scope the run?), leaving the agent to guess.

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?

States a specific verb (Get) and resource (findings of one comparison run), then enumerates the three categories of findings returned. This distinguishes it from list_model_diffs, which enumerates runs rather than reporting on one, though no sibling is named explicitly.

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?

No when-to-use guidance, no mention of prerequisites, and no routing to alternatives such as list_model_diffs. The agent must infer that a diff_id is obtained by first calling list_model_diffs.

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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