Loading ToolBench
Decoupled MVP framework for agent-assisted hyperspectral image classification research
HyperAgent exposes 39 tools across two modules (hermes_plugin and agent_tools). While input schemas are present with type definitions and descriptions, the tool definitions reveal significant gaps: (1) Many tool descriptions are generic or lack actionable context for LLM selection. (2) Parameter descriptions exist but are often minimal (10-30 chars), below the 72-char baseline. (3) Output schemas are not documented, callers cannot predict response structure. (4) Error handling is absent; no recovery guidance or categorization. (5) Tools like 'run_command' and 'apply_patch' are high-risk but lack confirmation/dry-run patterns. (6) No tool annotations (readOnlyHint, destructiveHint, idempotentHint) despite clear risk levels. (7) Composition issues: 'task' and 'run_skill' are vague orchestration tools that blur responsibility boundaries. Average per-tool score: 42/100.
Analyze spectral bands from a dataset audit and generate a spectral report
Apply a unified diff after permission and checkpoint.
Build an experiment plan for HSI model training based on audit, spectral report, and model recommendation
Build a markdown report from an experiment result
Validate a unified diff without applying it.
Alias for paper_strategy_compare.
Alias for research_experience_consolidate.
No output schemas documented. Tools return Dict[str, Any] with no specification of response structure, forcing LLMs to guess field names and types. Breaks tool chaining and increases hallucination risk.
High-risk tools (run_command, apply_patch, install_skill) lack confirmation/dry-run patterns. Agents can execute destructive operations without user approval or preview.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 46 | 2026-07-28+ | v2 |
Design automated HSI experiments with multiple model candidates
Search extracted baseline, ablation, control-variable, robustness, and visualization strategies.
Extract experiment-strategy lessons from one paper and write a HyperVault strategy card.
Extract research-pattern lessons from one paper and write a HyperVault strategy card.
Extract or mark underdetermined research-taste lessons for one paper.
Extract scientific-storytelling lessons from one paper and write a HyperVault strategy card.
Query safe HyperAgent framework status and registry features.
Create an image-edit request artifact.
Create an image-generation request artifact.
Inspect a hyperspectral image dataset and generate an audit report
Preflight or install a third-party SKILL.md skill from a local path or GitHub source.
Compare research strategies across papers without writing derived memory.
Propose a novel module for HSI classification based on literature and dataset characteristics
Propose parameter updates for HSI model based on experiment results
Read a text file inside the project root.
Recommend a hyperspectral image classification model based on dataset audit and spectral analysis
Write long-term research-experience memory from paper strategy cards.
Search extracted novelty, gap, contribution, and reviewer-expectation research patterns.
Search cross-paper research taste and field-timing lessons.
Run an allowlisted or user-authorized shell command.
Run a HyperAgent experiment YAML.
Run a baseline HSI classification experiment from an experiment plan
Run a SKILL.md skill inline or as a subagent.
Search text inside project files.
Search for hyperspectral image classification literature
Search extracted scientific storytelling and reviewer-persuasion strategies.
Run one or more subagents and aggregate their output.
Replace TodoWrite state.
List recent web citations saved by HyperAgent.
Fetch and extract readable text from a public HTTP(S) page.
Fetch a public HTTP(S) page, extract text, and save an artifact.
Search the web through a configured provider and save citations.
No tool annotations (readOnlyHint, destructiveHint, idempotentHint) despite clear risk levels. LLMs cannot distinguish safe reads from destructive writes without explicit hints.
Parameter descriptions are minimal (10-30 chars), below 72-char baseline. Examples: 'Path to file within project root' (read_file), 'Shell command to execute' (run_command). Lack actionable context for LLM selection and constraint guidance.
Vague tool names and descriptions. 'task' (run subagents), 'framework_command' (query framework), 'todo_write' (replace state) lack clarity on when to use them vs alternatives. Ambiguous names cause LLM confusion.