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cdash-mcp

by cbyrohl
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Python
Remote

get_dynamic_analysis

Retrieve dynamic analysis findings, including Valgrind and sanitizer issues, for a specific build to identify memory errors and undefined behavior.

Instructions

Get dynamic analysis results (e.g. Valgrind, sanitizers) for a build.

Args:
    build_id: The CDash build ID.
    limit: Maximum number of defect entries to return (default 50, max 200).
    offset: Number of defect entries to skip (default 0). Use for pagination.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
offsetNo
build_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior3/5

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

There are no annotations, so the description carries the behavioral disclosure burden. It discloses useful pagination behavior for limit/offset, including default and maximum values. It does not mention errors, empty results, or performance implications, but it does provide more than a bare getter statement.

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 compact: a one-sentence purpose followed by a clean Args block. Every line earns its place, and the key purpose is front-loaded before parameter details.

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-style getter with an output schema present, the description is complete. All three parameters are fully documented, the pagination semantics are clear, and the output schema covers return values. Nothing needed to call the tool correctly is missing.

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

Parameters5/5

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

Schema description coverage is 0%, so the description fully compensates. It explains build_id as the CDash build ID, limit as maximum defect entries with default and max, and offset as entries to skip with pagination guidance. This adds meaning far beyond the bare integer properties in the schema.

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?

The description states a specific verb and resource: "Get dynamic analysis results (e.g. Valgrind, sanitizers) for a build." This clearly differentiates the tool from siblings like get_build_errors, get_test_summary, and get_build_details, which target other build artifact types.

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

Usage Guidelines3/5

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

The description implies usage: an agent should call this when it needs dynamic analysis results (Valgrind, sanitizers) for a specific build. However, it does not explicitly compare against sibling tools or state when not to use this tool, leaving the routing decision mostly to the tool name and examples.

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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