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thinkdeep

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Analyzes complex problems through multi-stage investigation and reasoning. Supports architecture decisions, debugging, performance, and security analysis with hypothesis testing and expert validation.

Instructions

Performs multi-stage investigation and reasoning for complex problem analysis. Use for architecture decisions, complex bugs, performance challenges, and security analysis. Provides systematic hypothesis testing, evidence-based investigation, and expert validation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stepYesCurrent work step content and findings from your overall work
modelYesCurrently in auto model selection mode. CRITICAL: When the user names a model, you MUST use that exact name unless the server rejects it. If no model is provided, you may use the `listmodels` tool to review options and select an appropriate match. Top models: gemini-2.5-pro (score 100, 1.0M ctx, thinking, code-gen); gemini-3-pro-preview (score 100, 1.0M ctx, thinking, code-gen); gemini-2.5-flash (score 61, 1.0M ctx, thinking); gemini-2.0-flash (score 56, 1.0M ctx, thinking); gemini-2.0-flash-lite (score 42, 1.0M ctx).
imagesNoOptional absolute image paths or base64 blobs for visual context.
findingsYesImportant findings, evidence and insights discovered in this step
confidenceNoConfidence level: exploring (just starting), low (early investigation), medium (some evidence), high (strong evidence), very_high (comprehensive understanding), almost_certain (near complete confidence), certain (100% confidence locally - no external validation needed)
hypothesisNoCurrent theory about issue/goal based on work
focus_areasNoFocus aspects (architecture, performance, security, etc.)
step_numberYesCurrent step number in work sequence (starts at 1)
temperatureNo0 = deterministic · 1 = creative.
total_stepsYesEstimated total steps needed to complete work
issues_foundNoIssues identified with severity levels during work
files_checkedNoList of files examined during this work step
thinking_modeNoReasoning depth: minimal, low, medium, high, or max.
relevant_filesNoFiles identified as relevant to issue/goal (FULL absolute paths to real files/folders - DO NOT SHORTEN)
continuation_idNoUnique thread continuation ID for multi-turn conversations. Works across different tools. ALWAYS reuse the last continuation_id you were given—this preserves full conversation context, files, and findings so the agent can resume seamlessly.
problem_contextNoAdditional context about problem/goal. Be expressive.
relevant_contextNoMethods/functions identified as involved in the issue
next_step_requiredYesWhether another work step is needed. When false, aim to reduce total_steps to match step_number to avoid mismatch.
use_assistant_modelNoUse assistant model for expert analysis after workflow steps. False skips expert analysis, relies solely on your personal investigation. Defaults to True for comprehensive validation.
Behavior4/5

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

Annotations declare readOnlyHint=true, and the description reinforces this by describing analysis activities (investigation, reasoning, hypothesis testing) without any implication of state modification. The description adds behavioral context about multi-stage processing and expert validation that annotations alone do not provide.

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?

Three sentences with no fluff: first states purpose, second lists use cases, third summarizes capabilities. Every sentence adds value. The description is front-loaded with the most critical information.

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?

Despite having 19 parameters and no output schema, the description provides adequate high-level context about the tool's multi-stage nature and systematic approach. The schema fields (step, step_number, findings, etc.) further clarify the workflow. Missing details about output format are compensated by the schema's explicitness.

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 already documents all parameter meanings comprehensively. The description does not add parameter-level details beyond the schema, but that is acceptable since the schema descriptions are thorough. Baseline 3 applies.

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 clearly states the tool's specific verb-resource combination: 'Performs multi-stage investigation and reasoning' for complex problem analysis. It lists concrete use cases (architecture decisions, complex bugs, performance challenges, security analysis), effectively distinguishing it from sibling tools like analyze, debug, or secaudit which are more focused or single-stage.

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 provides clear, actionable guidance on when to use this tool by listing appropriate scenarios. However, it does not explicitly state when not to use it (e.g., simple lookups) or compare to similar siblings. Despite this, the context is sufficiently clear for an AI agent to make appropriate selection.

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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