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

compare_responses

Read-onlyIdempotent

Compare two ALREADY-PRODUCED outputs (e.g. model A vs model B on the same task) side by side. Returns deterministic metrics (token cosine, ROUGE-L, Jaccard, length/structure deltas, JSON diff) and a verdict. If a reference (ground truth) is given, scores each output against it and picks the closer one. If model + api_key are given, an LLM judge also picks a qualitative winner for the task. No re-execution — you bring the outputs.

Input Schema

TableJSON Schema

Name	Required	Description
`task`	No	The task/prompt both outputs were answering — used by the LLM judge for context
`model`	No	Optional judge model id (BYOK). When set with api_key, an LLM judge picks a qualitative winner.
`api_key`	No	Optional API key for the judge model (BYOK). Used only for the judge call; never stored.
`label_a`	No	Label for output A (e.g. "GPT-4o", "v1.0")
`label_b`	No	Label for output B (e.g. "GPT-5-nano", "v1.1")
`reference`	No	Optional ground-truth / expected answer. If set, each output is scored against it and the closer one wins (deterministic).
`check_json`	No	Try to parse as JSON and compare structurally (keys, types, values)
`response_a`	Yes	First output (e.g. model A's answer)
`response_b`	Yes	Second output (e.g. model B's answer)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`judge`	No
`labelA`	No
`labelB`	No
`metrics`	No
`summary`	No
`verdict`	No

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed12 schema fields changedJun 27, 2026

addedInput schema / properties / api_key

Added value: +{
+  "description": "Optional API key for the judge model (BYOK). Used only for the judge call; never stored.",
+  "type": "string"
+}

changedInput schema / properties / label_a / description

Previous value: -"Label for response A (e.g. \"GPT-4o\", \"v1.0\")"New value: +"Label for output A (e.g. \"GPT-4o\", \"v1.0\")"

changedInput schema / properties / label_b / description

Previous value: -"Label for response B (e.g. \"Claude\", \"v1.1\")"New value: +"Label for output B (e.g. \"GPT-5-nano\", \"v1.1\")"

addedInput schema / properties / model

Added value: +{
+  "description": "Optional judge model id (BYOK). When set with api_key, an LLM judge picks a qualitative winner.",
+  "type": "string"
+}

addedInput schema / properties / reference

Added value: +{
+  "description": "Optional ground-truth / expected answer. If set, each output is scored against it and the closer one wins (deterministic).",
+  "type": "string"
+}

changedInput schema / properties / response_a / description

Previous value: -"First response (baseline / control)"New value: +"First output (e.g. model A's answer)"

changedInput schema / properties / response_b / description

Previous value: -"Second response (variant / test)"New value: +"Second output (e.g. model B's answer)"

addedInput schema / properties / task

Added value: +{
+  "description": "The task/prompt both outputs were answering — used by the LLM judge for context",
+  "type": "string"
+}

removedOutput schema / properties / diffs
```
Removed value: -{}
```
addedOutput schema / properties / judge
```
Added value: +{}
```
addedOutput schema / properties / metrics
```
Added value: +{}
```
addedOutput schema / properties / verdict
```
Added value: +{}
```

Changed1 schema field changedJun 21, 2026

addedOutput schema / properties

Added value: +{
+  "diffs": {},
+  "labelA": {},
+  "labelB": {},
+  "summary": {
+    "type": "string"
+  }
+}

Changed1 schema field changedJun 10, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "type": "object"
+}

First observedMay 26, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive behavior. The description adds valuable behavioral context beyond that: it lists exactly what metrics are returned, explains that a `reference` causes scoring against ground truth, and that providing `model`+`api_key` triggers an LLM judge call. It also notes 'No re-execution' and the schema notes the API key is 'never stored,' which bolsters transparency.

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—five sentences that front-load the core purpose, then layer conditional behavior and end with a clear constraint. Every sentence earns its place; no filler or redundancy.

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?

Given an output schema, rich annotations, and 100% schema parameter coverage, the description is complete enough. It covers the two major optional modes (reference-based scoring and LLM judging), the deterministic metrics, and the no-re-execution constraint, leaving no significant gaps for a tool of this complexity.

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

Parameters4/5

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

Schema coverage is 100% with good per-parameter descriptions, yielding a baseline of 3. The description adds semantic value by explaining how parameters interact: `reference` makes each output scored against it, `model`+`api_key` enables the judge, and the metric list (token cosine, ROUGE-L, Jaccard, JSON diff) clarifies what the tool computes from `response_a` and `response_b`.

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 opens with a specific verb and resource: 'Compare two ALREADY-PRODUCED outputs ... side by side.' It clearly differentiates from siblings by emphasizing no re-execution and listing concrete deterministic metrics plus an optional LLM judge verdict.

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?

The description clearly states when to use the tool: when outputs already exist and you want a side-by-side comparison. It gives conditional guidance for `reference` and `model`+`api_key`, and explicitly says 'No re-execution — you bring the outputs,' which implies it is not for generating outputs. It does not name alternatives explicitly, so it's strong context without formal exclusions.

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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