IA-QA — 130+ QA & Dev Tools for AI Agents

validate_mcp_response

Read-onlyIdempotent

Validate that an MCP tool response conforms to expected format, schema, and content rules. Use this to QA-test any MCP server tool. Supply the tool's actual JSON result and a set of checks to perform.

Input Schema

TableJSON Schema

Name	Required	Description
`response`	Yes	The MCP tool result as a JSON string to validate
`min_items`	No	If response is an array, minimum number of items expected
`expected_type`	No	Expected top-level type: "object", "array", "string", "number"
`required_keys`	No	Comma-separated list of keys that MUST exist in the response (dot-notation for nested: "data.id, data.name")
`actual_latency`	No	Actual measured latency in ms (from the call)
`forbidden_keys`	No	Comma-separated list of keys that MUST NOT exist (e.g. "password, secret, token")
`max_size_bytes`	No	Maximum acceptable response size in bytes
`max_response_ms`	No	Maximum acceptable latency in ms (will be compared if provided)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`total`	No
`checks`	No
`failed`	No
`passed`	No
`verdict`	No

TDQS

A3.8/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows this is a safe read-only operation. The description adds that it performs checks on a provided response, which is useful but does not elaborate on error handling or output behavior beyond what annotations imply.

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 two sentences long, front-loaded with the core purpose, and every word earns its place. No filler or redundant detail.

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?

Given the output schema exists and parameter coverage is 100%, the description sufficiently covers the tool's purpose and usage. It could briefly mention that the tool does not call the MCP server itself, but this is not essential for selection or invocation.

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 fully documents all parameters. The description only generically references 'checks to perform,' adding no meaningful detail beyond the schema. Baseline 3 is appropriate.

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 the tool validates MCP tool responses against format, schema, and content rules. It names the specific resource (MCP tool response) and the action (validate), but does not explicitly distinguish it from sibling validation tools like json_schema_validate or mcp_schema_lint.

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 provides clear usage context: 'Use this to QA-test any MCP server tool.' This tells the agent when to use the tool, but it does not mention exclusions or explicitly compare to alternatives, falling short of a 5.

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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TDQS

A3.6/5.0

Disambiguation2/5

Multiple tools overlap significantly: compare_models/llm_fit_finder/model_info/list_llm_models all compare models; similarity_score/embedding_similarity/run_semantic_tests all measure text similarity; detect_secrets/secret_scan/analyze_diff_bugs/pr_gatekeeper all scan for secrets. Descriptions attempt to differentiate, but the boundaries between many tools are unclear, making selection error-prone.

Naming Consistency4/5

The vast majority of tools follow a snake_case verb_noun pattern (validate_email, generate_uuid, parse_csv), making the set mostly predictable. A few notable deviations exist (pr_gatekeeper, llm_fit_finder, cot_analyzer, jira_to_test_suite, needle_haystack_generate) but they are the exception rather than the rule.

Tool Count1/5

With 149 tools, this set is far beyond the 50+ threshold for an extreme mismatch. Even as a general-purpose QA & Dev toolkit, the sheer number overwhelms and exceeds any reasonable scope, making discovery and selection impractical.

Completeness4/5

The toolkit covers an impressively broad range: text processing, LLM evaluation, security auditing, web checks, MCP validation, Jira/Confluence integration, and more. Minor gaps exist, such as missing delete/update for webhooks and Confluence pages, and no create/update for Jira issues, but these are workable around.

Resources

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