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

rerank_evaluate

Read-only

Evaluate RAG retrieval quality: rank passages against a query and compute Precision@k / Recall@k plus a PASS/FAIL CI verdict from ground-truth relevance labels. Three modes, all keyless except the last. (1) BYO scores — give each passage the score your own reranker produced (Cohere, Jina, a self-hosted NIM, a cross-encoder): deterministic, offline, and it evaluates YOUR reranker rather than someone else's. This is the mode to gate CI on. (2) Default, no scores and no key — ranks with local BM25, a lexical keyword baseline: it answers "does a keyword floor already surface my relevant passages?", never "is my neural reranker good". (3) Live NVIDIA reranker — supply api_key for an NVIDIA account that still has reranking entitlement; NVIDIA retired its hosted reranking endpoints on 2026-05-18, so this one is for accounts that were grandfathered in.

Input Schema

TableJSON Schema

Name	Required	Description
`query`	Yes	The search query or question to rank against
`top_k`	No	k for Precision@k evaluation (default 3)
`api_key`	No	Your NVIDIA API key (BYOK), used only when no passage carries a score. Transits RAM for the single call, never stored.
`passages`	Yes	Array of passage objects to rank (min 2, max 20)
`threshold`	No	Minimum Precision@k to PASS (0-1, default 0.5)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`k`	No
`mode`	No
`model`	No
`ranked`	No
`verdict`	No
`threshold`	No
`latency_ms`	No
`recall_at_k`	No
`precision_at_k`	No
`total_passages`	No
`has_ground_truth`	No

Schema Changelog

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

Changed15 schema fields changedAug 20, 2026

addedInput schema / properties / api_key

Added value: +{
+  "description": "Your NVIDIA API key (BYOK), used only when no passage carries a score. Transits RAM for the single call, never stored.",
+  "type": "string"
+}

addedInput schema / properties / passages / items / properties / score

Added value: +{
+  "description": "Relevance score from YOUR reranker. Present on every passage → ranking is done from these (offline, no key). Higher = more relevant. Score all passages or none.",
+  "type": "number"
+}

addedOutput schema / properties / has_ground_truth
```
Added value: +{
+  "type": "boolean"
+}
```
addedOutput schema / properties / k
```
Added value: +{
+  "type": "number"
+}
```
addedOutput schema / properties / latency_ms
```
Added value: +{
+  "type": "number"
+}
```
addedOutput schema / properties / mode
```
Added value: +{
+  "type": "string"
+}
```
addedOutput schema / properties / precision_at_k
```
Added value: +{
+  "type": "number"
+}
```

removedOutput schema / properties / query

Removed value: -{
-  "type": "string"
-}

addedOutput schema / properties / ranked

Added value: +{
+  "items": {
+    "type": "object"
+  },
+  "type": "array"
+}

addedOutput schema / properties / recall_at_k
```
Added value: +{
+  "type": "number"
+}
```

removedOutput schema / properties / results

Removed value: -{
-  "items": {
-    "type": "object"
-  },
-  "type": "array"
-}

addedOutput schema / properties / threshold
```
Added value: +{
+  "type": "number"
+}
```

removedOutput schema / properties / top_n

Removed value: -{
-  "type": "number"
-}

addedOutput schema / properties / total_passages
```
Added value: +{
+  "type": "number"
+}
```
addedOutput schema / properties / verdict
```
Added value: +{
+  "type": "string"
+}
```

Changed1 schema field changedJun 21, 2026

addedOutput schema / properties

Added value: +{
+  "model": {
+    "type": "string"
+  },
+  "query": {
+    "type": "string"
+  },
+  "results": {
+    "items": {
+      "type": "object"
+    },
+    "type": "array"
+  },
+  "top_n": {
+    "type": "number"
+  }
+}

Changed1 schema field changedJun 10, 2026

changedOutput schema / (root)

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

First observedMay 26, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Despite good annotations, the description adds substantial behavioral context: BYO mode is deterministic/offline, BM25 mode is a lexical baseline with no neural evaluation, the NVIDIA endpoint was retired on 2026-05-18, and the API key 'transits RAM for the single call, never stored.' This fully discloses side effects and state implications beyond the annotations.

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 structured as a numbered list with a front-loaded purpose. It is detailed but every sentence earns its place, including context like the NVIDIA retirement date and key-storage behavior. 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?

The tool has three distinct execution modes, CI-gating semantics, and a historical deprecation caveat, all fully explained. Output schema exists, so return-value documentation is not required here. The description is complete for the tool's 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%, so the baseline is 3, but the description adds meaningful semantic guidance: score must be present on every passage or none, api_key is used only when no passage carries a score, and higher scores mean more relevant. These details connect the parameters to mode selection in a way the schema alone does not.

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: 'Evaluate RAG retrieval quality: rank passages against a query and compute Precision@k / Recall@k plus a PASS/FAIL CI verdict.' It clearly differentiates three modes and is distinct from sibling tools like rag_relevance_rank or bm25_score by emphasizing ground-truth evaluation rather than mere ranking.

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

Usage Guidelines5/5

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

The description gives explicit when-to-use guidance for each mode: BYO scores is 'the mode to gate CI on', BM25 mode answers a specific keyword-floor question and 'never is my neural reranker good', and the live NVIDIA mode is restricted to grandfathered accounts with reranking entitlement. This is exemplary usage differentiation.

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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