The Clear Thought MCP Server provides a suite of structured reasoning tools to enhance problem-solving. You can:
Apply mental models (e.g., First Principles, Occam's Razor, Pareto Principle) for systematic thinking.
Implement design patterns (e.g., Modular Architecture, State Management, Security Best Practices) for software design.
Utilize programming paradigms (e.g., Object-Oriented, Functional, Event-Driven) with code examples and trade-offs.
Employ debugging approaches (e.g., Binary Search, Divide and Conquer, Program Slicing) for systematic issue resolution.
Engage in sequential thinking to break down complex problems step-by-step, with branching and revision tracking.
Facilitate collaborative reasoning via multi-persona simulation, ideation, critique, and consensus building.
Use decision frameworks (e.g., weighted criteria, decision trees, scenario analysis) for structured decision-making.
Perform metacognitive monitoring to assess knowledge boundaries, bias detection, and confidence calibration.
Apply the scientific method with hypothesis testing, experiment design, and evidence evaluation.
Conduct structured argumentation using thesis-antithesis-synthesis and logical premise evaluation.
Employ visual reasoning to create and manipulate diagrams, flowcharts, and graphs for spatial problem-solving.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Clear Thought MCP Server help me debug this API integration using first principles thinking"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Systematic thinking, mental models, and debugging approaches — as MCP tools for any AI client.
An MCP server that gives an AI assistant a toolbox of structured reasoning frameworks — mental models, design patterns, systematic debugging, decision analysis, the scientific method, and more — so it reasons deliberately instead of improvising. Each mode is an explicit, inspectable tool the model calls, with typed (Zod-validated) inputs and a formatted, chain-of-thought-friendly response.
Twelve tools, one server, zero required configuration.
Related MCP server: Clear Thought 1.5
Live MCP endpoint: https://clear-thought-mcp-server.oriz.in (Streamable HTTP, MCP 2.0)
GitHub Pages: https://chirag127.github.io/Clear-Thought-MCP-server/
Smithery: https://smithery.ai/server/@chirag127/clear-thought-mcp-server
⭐ If this is useful, please star the repo — it helps others find it.
flowchart LR
Client["MCP Client<br/>(Claude, Cursor, opencode, ...)"]
subgraph Server["clear-thought-mcp-server"]
direction TB
T1["sequentialthinking"]
T2["mentalmodel"]
T3["designpattern"]
T4["programmingparadigm"]
T5["debuggingapproach"]
T6["collaborativereasoning"]
T7["decisionframework"]
T8["metacognitivemonitoring"]
T9["scientificmethod"]
T10["structuredargumentation"]
T11["visualreasoning"]
T12["stochasticthinking"]
end
Client -->|"Streamable HTTP (MCP 2.0) /mcp<br/>or stdio"| Server
Server -->|"formatted reasoning result"| ClientTool | What it does |
| Dynamic, reflective step-by-step problem solving with revisions & branches |
| Apply a named model (first principles, Occam's razor, Pareto, opportunity cost, error propagation, rubber duck) |
| Software architecture patterns (modular, API integration, state, async, scalability, security, agentic) |
| Reason through imperative / OO / functional / reactive / concurrent … paradigms |
| Systematic debugging (binary search, reverse engineering, divide & conquer, backtracking, cause elimination, program slicing) |
| Simulate a panel of expert personas with diverse perspectives |
| Structured decision analysis and rational choice |
| Self-monitor knowledge boundaries and reasoning quality |
| Formal hypothesis → experiment → analysis loop |
| Dialectical reasoning — claims, rebuttals, synthesis |
| Create/manipulate diagrams and visual representations |
| Probabilistic reasoning over uncertain problems |
12 reasoning tools, each with a typed Zod input schema and formatted output
Two transports: stdio (local clients) and Streamable HTTP (MCP 2.0, remote)
Zero required configuration — no keys, no state, no external calls
Installable via npx, Smithery, or the hosted endpoint
TypeScript + Node.js (ESM, >=18)
zod for input validation, chalk for formatting
vitest for tests, tsc build
Clear-Thought-MCP-server/
├── src/
│ ├── index.ts # server bootstrap, tool registration, stdio + HTTP transports
│ ├── models/interfaces.ts # shared TypeScript interfaces
│ └── tools/ # one module per reasoning tool (mentalModelServer.ts, ...)
├── dist/ # compiled JS (tsc output)
├── test/ # vitest suites
├── Dockerfile # container image
├── smithery.yaml # Smithery deploy config
└── package.jsonnpx clear-thought-mcp-servernpx -y @smithery/cli install @chirag127/clear-thought-mcp-server --client claudeHosted (Streamable HTTP):
{
"mcpServers": {
"clear-thought": {
"url": "https://clear-thought-mcp-server.oriz.in/mcp"
}
}
}Local (stdio via npx):
{
"mcpServers": {
"clear-thought": {
"command": "npx",
"args": ["-y", "clear-thought-mcp-server"]
}
}
}npm install
npm run build
HTTP_PORT=3779 npm start # serves Streamable HTTP at http://localhost:3779/mcpMCP Registry: https://registry.modelcontextprotocol.io
Smithery: @chirag127/clear-thought-mcp-server — https://smithery.ai/server/@chirag127/clear-thought-mcp-server
No configuration is required. Optional environment variables:
Variable | Purpose |
| Port for the Streamable HTTP transport (default |
One of ~80 sites and tools in the oriz family. It reasons; the Stochastic Thinking MCP Server handles probabilistic decision-making, and knowledge-mcp serves the knowledge base.
Issues and PRs welcome. Conventional commits are the changelog.
MIT — see LICENSE.
Chirag Singhal · chirag@oriz.in · @chirag127
Stable (v1.1.2). Roadmap: more mental models, richer visual-reasoning output.
A detailed tool for simulating expert collaboration with diverse perspectives. This tool helps models tackle complex problems by coordinating multiple viewpoints. It provides a framework for structured collaborative reasoning and perspective integration.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | ||
| personas | Yes | ||
| contributions | Yes | ||
| stage | Yes | ||
| activePersonaId | Yes | ||
| nextPersonaId | No | ||
| consensusPoints | No | ||
| disagreements | No | ||
| keyInsights | No | ||
| openQuestions | No | ||
| finalRecommendation | No | ||
| sessionId | Yes | Unique identifier for this collaboration session | |
| iteration | Yes | Current iteration of the collaboration | |
| suggestedContributionTypes | No | ||
| nextContributionNeeded | Yes | Whether another contribution is needed |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the tool's function as a simulation framework but lacks details on operational traits: it doesn't mention if it's stateful (e.g., requires session management), has side effects, involves iterative processes, or handles errors. For a complex tool with 15 parameters, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with three sentences that are front-loaded: the first sentence introduces the core purpose, followed by supporting details. There's no unnecessary repetition or fluff, making it efficient, though it could be more structured with bullet points or clearer sections given the complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the high complexity (15 parameters, nested structures), lack of annotations, and no output schema, the description is incomplete. It doesn't address how the tool behaves, what it returns, or detailed usage scenarios. For a sophisticated simulation tool, this leaves critical gaps in understanding its full context and operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is low at 20%, with only a few parameters like 'sessionId' and 'iteration' having descriptions. The tool description adds no specific meaning about parameters beyond the general context of collaboration. It doesn't explain how parameters like 'personas' or 'contributions' should be structured or used, leaving most semantics undocumented. This meets the baseline for minimal compensation given the coverage gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool is for 'simulating expert collaboration with diverse perspectives' and 'tackling complex problems by coordinating multiple viewpoints,' which gives a general purpose. However, it's vague about the specific action (e.g., whether it initiates, continues, or summarizes collaboration) and doesn't clearly distinguish it from sibling tools like 'structuredargumentation' or 'decisionframework,' which might overlap in problem-solving contexts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance is provided on when to use this tool versus alternatives. The description mentions 'complex problems' and 'structured collaborative reasoning,' but it doesn't specify prerequisites, exclusions, or compare it to siblings such as 'sequentialthinking' or 'metacognitivemonitoring.' This leaves the agent without clear direction for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
A tool for applying systematic debugging approaches to solve technical issues. Supports various debugging methods including:
Binary Search
Reverse Engineering
Divide and Conquer
Backtracking
Cause Elimination
Program Slicing
Each approach provides a structured method for identifying and resolving issues.
| Name | Required | Description | Default |
|---|---|---|---|
| approachName | Yes | ||
| issue | Yes | ||
| steps | No | ||
| findings | No | ||
| resolution | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that approaches provide 'structured methods for identifying and resolving issues,' which hints at a process-oriented tool, but lacks critical details: it doesn't specify if this is a read-only analysis tool or if it modifies data, what the output format looks like, any rate limits, or authentication needs. For a tool with 5 parameters and no annotation coverage, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the core purpose in the first sentence. The bulleted list of methods is efficient for enumeration, and the concluding sentence reinforces the tool's value. There's no redundant information, though it could be slightly more streamlined by integrating the list into the flow.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (5 parameters, 0% schema coverage, no output schema, no annotations), the description is incomplete. It doesn't explain what the tool returns, how parameters interact, or the behavioral implications of using different debugging approaches. For a tool that likely involves multi-step reasoning processes, more context on execution and results is needed to be fully helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for undocumented parameters. It lists debugging method names (e.g., 'Binary Search') which partially explains the 'approachName' enum, but doesn't clarify the semantics of other parameters like 'issue', 'steps', 'findings', or 'resolution'. The description adds minimal value beyond what the enum suggests, failing to adequately cover the parameter meanings.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'applying systematic debugging approaches to solve technical issues.' It specifies the verb ('applying') and resource ('debugging approaches'), making it distinct from sibling tools like 'collaborativereasoning' or 'designpattern' which focus on different reasoning methods. However, it doesn't explicitly differentiate itself from all siblings (e.g., 'sequentialthinking' might overlap), preventing a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It lists supported debugging methods but doesn't specify scenarios where this tool is appropriate compared to sibling tools like 'scientificmethod' or 'structuredargumentation'. There's no mention of prerequisites, exclusions, or comparative contexts, leaving usage ambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
A detailed tool for structured decision analysis and rational choice. This tool helps models systematically evaluate options, criteria, and outcomes. It supports multiple decision frameworks, probability estimates, and value judgments.
| Name | Required | Description | Default |
|---|---|---|---|
| decisionStatement | Yes | ||
| options | Yes | ||
| criteria | No | ||
| analysisType | Yes | ||
| stage | Yes | ||
| stakeholders | No | ||
| constraints | No | ||
| timeHorizon | No | ||
| riskTolerance | No | ||
| possibleOutcomes | No | ||
| recommendation | No | ||
| rationale | No | ||
| decisionId | Yes | Unique identifier for this decision analysis | |
| iteration | Yes | Current iteration of the decision process | |
| nextStageNeeded | Yes | Whether another stage is needed in the process |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure but offers minimal insight. It mentions 'systematically evaluate' and 'supports multiple decision frameworks' but doesn't describe what the tool actually does operationally—whether it performs calculations, generates reports, stores data, or requires specific permissions. For a complex tool with 15 parameters, this is inadequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in three sentences that each add value: establishing purpose, core functions, and capabilities. There's no redundant information, though it could be more front-loaded with concrete action verbs. The length is appropriate for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex tool with 15 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool produces, how it handles the extensive input data, or what behavioral outcomes to expect. The gap between the description's generality and the schema's specificity creates ambiguity about the tool's actual operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 20%, so the description must compensate but fails to do so. It vaguely references 'options, criteria, and outcomes' and 'probability estimates, and value judgments' which map to some parameters, but doesn't explain the purpose or relationships of the 15 parameters. Key parameters like 'analysisType', 'stage', and 'possibleOutcomes' remain semantically unclear from the description alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'structured decision analysis and rational choice' with specific functions like evaluating options, criteria, and outcomes. It distinguishes from siblings by focusing on decision frameworks rather than reasoning methods, though it doesn't explicitly contrast with tools like 'structuredargumentation' or 'collaborativereasoning'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions supporting 'multiple decision frameworks' but doesn't specify scenarios where this tool is preferred over sibling tools like 'structuredargumentation' or 'mentalmodel', nor does it indicate prerequisites or exclusions for its use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
A tool for applying design patterns to software architecture and implementation. Supports various design patterns including:
Modular Architecture
API Integration Patterns
State Management
Asynchronous Processing
Scalability Considerations
Security Best Practices
Agentic Design Patterns
Each pattern provides a structured approach to solving common design challenges.
| Name | Required | Description | Default |
|---|---|---|---|
| patternName | Yes | ||
| context | Yes | ||
| implementation | No | ||
| benefits | No | ||
| tradeoffs | No | ||
| codeExample | No | ||
| languages | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'applying design patterns' and lists pattern types but doesn't describe what the tool actually does (e.g., generates output, modifies code, provides advice), its limitations, or any behavioral traits like side effects or performance. This leaves significant gaps in understanding the tool's operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with two paragraphs: the first states the purpose and lists patterns, and the second explains the value. It's front-loaded with key information, and while the bulleted list is detailed, it serves to clarify scope without unnecessary verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (7 parameters, no annotations, no output schema), the description is incomplete. It lacks details on what the tool outputs, how it behaves, and full parameter explanations. For a tool with this many inputs and no structured support, more comprehensive guidance is needed to ensure effective use by an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for 7 undocumented parameters. It lists pattern categories that correspond to the 'patternName' enum values, adding some meaning, but doesn't explain other parameters like 'context', 'implementation', 'benefits', 'tradeoffs', 'codeExample', or 'languages'. This partial coverage is insufficient given the low schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool is for 'applying design patterns to software architecture and implementation' and lists specific pattern categories, which provides a general purpose. However, it doesn't specify what 'applying' means operationally (e.g., generates documentation, suggests implementations, analyzes code) or how it differs from sibling tools like 'programmingparadigm' or 'structuredargumentation', making it somewhat vague.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives is provided. The description mentions 'solving common design challenges' but doesn't specify contexts, prerequisites, or exclusions. Given sibling tools like 'debuggingapproach' and 'decisionframework', there's no differentiation, leaving the agent with minimal usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
A tool for applying structured mental models to problem-solving. Supports various mental models including:
First Principles Thinking
Opportunity Cost Analysis
Error Propagation Understanding
Rubber Duck Debugging
Pareto Principle
Occam's Razor
Each model provides a systematic approach to breaking down and solving problems.
| Name | Required | Description | Default |
|---|---|---|---|
| modelName | Yes | ||
| problem | Yes | ||
| steps | No | ||
| reasoning | No | ||
| conclusion | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the tool's function but lacks critical behavioral details: it doesn't specify whether this is a read-only analysis tool or if it modifies data, what the output format might be (e.g., structured analysis, recommendations), or any constraints like rate limits or authentication needs. The description is functional but misses key operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and well-structured: it starts with a clear purpose statement, lists supported models in a bulleted format for readability, and ends with a summary sentence. Every sentence adds value without redundancy. Minor improvement could be made by front-loading key usage details, but it's efficient overall.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (5 parameters, 0% schema coverage, no output schema, no annotations), the description is incomplete. It covers the tool's purpose and model examples but fails to address parameter meanings, output expectations, or behavioral traits. For a tool with multiple undocumented inputs and no structured output, more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for undocumented parameters. It lists six mental models by name, which clarifies the 'modelName' enum values, adding meaningful context beyond the raw enum list. However, it doesn't explain the semantics of other parameters like 'steps,' 'reasoning,' or 'conclusion,' leaving them ambiguous. The partial compensation justifies a baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'applying structured mental models to problem-solving' with a specific verb ('applying') and resource ('mental models'). It lists six concrete examples of supported models, making the purpose concrete. However, it doesn't explicitly differentiate from sibling tools like 'decisionframework' or 'structuredargumentation', which likely have overlapping problem-solving applications.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions 'systematic approach to breaking down and solving problems,' but this is generic and doesn't help an agent choose between this and sibling tools like 'collaborativereasoning' or 'scientificmethod.' No explicit when/when-not scenarios or prerequisites are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
A detailed tool for systematic self-monitoring of knowledge and reasoning quality. This tool helps models track knowledge boundaries, claim certainty, and reasoning biases. It provides a framework for metacognitive assessment across various domains and reasoning tasks.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | ||
| stage | Yes | ||
| knowledgeAssessment | No | ||
| claims | No | ||
| reasoningSteps | No | ||
| overallConfidence | Yes | ||
| uncertaintyAreas | Yes | ||
| recommendedApproach | Yes | ||
| monitoringId | Yes | Unique identifier for this monitoring session | |
| iteration | Yes | Current iteration of the monitoring process | |
| suggestedAssessments | No | ||
| nextAssessmentNeeded | Yes | Whether further assessment is needed |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While it mentions the tool 'provides a framework for metacognitive assessment', it doesn't describe what the tool actually does with the input data - whether it analyzes, stores, returns recommendations, or generates reports. There's no information about side effects, persistence, or what constitutes a successful invocation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with three sentences that each add value. It's front-loaded with the core purpose, followed by specific functions, and ends with the scope of application. There's no wasted text, though it could be slightly more structured with bullet points or clearer separation of concepts.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex tool with 12 parameters, nested objects, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns, how the monitoring framework operates, or how the various parameters interact. The description mentions a 'framework' but doesn't describe its structure or outputs, leaving 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.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 25%, but the description doesn't compensate by explaining any parameters. It mentions general concepts like 'knowledge boundaries, claim certainty, and reasoning biases' which loosely map to some parameters (knowledgeAssessment, claims, reasoningSteps), but provides no specific guidance on how to use these parameters or their relationships. The baseline is 3 since the description adds minimal value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'systematic self-monitoring of knowledge and reasoning quality' with specific functions like tracking knowledge boundaries, claim certainty, and reasoning biases. It distinguishes itself from sibling tools by focusing on metacognitive assessment rather than collaborative reasoning, debugging, or other specific methodologies. However, it doesn't explicitly contrast with all siblings in the list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions the tool is for 'various domains and reasoning tasks' but provides no explicit guidance on when to use this tool versus alternatives like 'collaborativereasoning' or 'debuggingapproach'. There's no mention of prerequisites, specific scenarios where metacognitive monitoring is preferred, or when other reasoning frameworks might be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
A tool for applying different programming paradigms to solve problems. Supports various programming paradigms including:
Imperative Programming
Procedural Programming
Object-Oriented Programming
Functional Programming
Declarative Programming
Logic Programming
Event-Driven Programming
Aspect-Oriented Programming
Concurrent Programming
Reactive Programming
Each paradigm provides a different approach to structuring and executing code.
| Name | Required | Description | Default |
|---|---|---|---|
| paradigmName | Yes | ||
| problem | Yes | ||
| approach | No | ||
| benefits | No | ||
| limitations | No | ||
| codeExample | No | ||
| languages | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but lacks behavioral details. It doesn't disclose what the tool does (e.g., generates explanations, provides code snippets, compares paradigms), how it handles inputs/outputs, or any constraints like rate limits or authentication needs. The phrase 'applying different programming paradigms' is too abstract to infer concrete behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with the main purpose in the first sentence, followed by a bulleted list of paradigms and a concluding sentence. There's no wasted text, though it could be more specific to enhance clarity without adding bulk.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 7 parameters with 0% schema coverage, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns, how parameters interact, or the complexity involved in applying paradigms, making it inadequate for an agent to use the tool effectively without guesswork.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate but fails to do so. It lists paradigm names but doesn't explain parameters like 'problem', 'approach', 'benefits', 'limitations', 'codeExample', or 'languages', leaving their purposes and formats unclear. The description adds minimal value beyond the enum for 'paradigmName'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'applies different programming paradigms to solve problems' and lists supported paradigms, which gives a general purpose. However, it's vague about what 'applying' entails (e.g., generating code, explaining concepts, comparing paradigms) and doesn't differentiate from siblings like 'designpattern' or 'structuredargumentation' that might also involve problem-solving approaches.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It doesn't specify scenarios where programming paradigms are appropriate (e.g., for coding tasks vs. theoretical discussions) or mention sibling tools, leaving the agent to guess based on context alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
A detailed tool for applying formal scientific reasoning to questions and problems. This tool guides models through the scientific method with structured hypothesis testing. It enforces explicit variable identification, prediction making, and evidence evaluation.
| Name | Required | Description | Default |
|---|---|---|---|
| stage | Yes | ||
| observation | No | ||
| question | No | ||
| hypothesis | No | ||
| experiment | No | ||
| analysis | No | ||
| conclusion | No | ||
| inquiryId | Yes | Unique identifier for this scientific inquiry | |
| iteration | Yes | Current iteration of the scientific process | |
| nextStageNeeded | Yes | Whether another stage is needed in the process |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool 'guides models through the scientific method' and 'enforces explicit variable identification, prediction making, and evidence evaluation,' which suggests it's a structured reasoning process rather than a data operation. However, it doesn't disclose whether this is a read-only tool, whether it stores or modifies data, what permissions might be needed, or what the output format looks like. For a complex tool with 10 parameters, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with three sentences that efficiently convey the tool's purpose and approach. It's front-loaded with the main function ('applying formal scientific reasoning') and follows with supporting details. There's no wasted text, though it could be slightly more structured with bullet points given the complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the high complexity (10 parameters, nested objects, no output schema, and 30% schema coverage), the description is insufficiently complete. It doesn't explain how to use the tool across multiple stages, what the expected inputs/outputs are, or how the scientific method iteration works in practice. For such a sophisticated tool, the description should provide more context about workflow and expected outcomes.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 30%, so the description should compensate but doesn't. The description mentions 'structured hypothesis testing,' 'variable identification,' 'prediction making,' and 'evidence evaluation,' which loosely map to some parameters like 'hypothesis' and 'experiment,' but it provides no specific guidance on parameter usage, relationships between parameters, or how to structure the complex nested objects. The description adds minimal value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'applying formal scientific reasoning to questions and problems' and 'guides models through the scientific method with structured hypothesis testing.' It specifies the verb ('guides through') and resource ('scientific method'), and distinguishes from siblings by focusing on formal scientific reasoning rather than other reasoning approaches like collaborative reasoning or debugging.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions 'structured hypothesis testing' but doesn't specify contexts where scientific reasoning is preferred over other reasoning tools like 'collaborativereasoning' or 'decisionframework.' There are no explicit when/when-not instructions or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
A detailed tool for dynamic and reflective problem-solving through thoughts. This tool helps analyze problems through a flexible thinking process that can adapt and evolve. Each thought can build on, question, or revise previous insights as understanding deepens.
When to use this tool:
Breaking down complex problems into steps
Planning and design with room for revision
Analysis that might need course correction
Problems where the full scope might not be clear initially
Problems that require a multi-step solution
Tasks that need to maintain context over multiple steps
Situations where irrelevant information needs to be filtered out
You should:
Start with an initial estimate of needed thoughts, but be ready to adjust
Feel free to question or revise previous thoughts
Don't hesitate to add more thoughts if needed, even at the "end"
Express uncertainty when present
Mark thoughts that revise previous thinking or branch into new paths
Ignore information that is irrelevant to the current step
Generate a solution hypothesis when appropriate
Verify the hypothesis based on the Chain of Thought steps
Repeat the process until satisfied with the solution
Provide a single, ideally correct answer as the final output
Only set next_thought_needed to false when truly done and a satisfactory answer is reached
| Name | Required | Description | Default |
|---|---|---|---|
| thought | Yes | ||
| thoughtNumber | Yes | ||
| totalThoughts | Yes | ||
| nextThoughtNeeded | Yes | ||
| isRevision | No | ||
| revisesThought | No | ||
| branchFromThought | No | ||
| branchId | No | ||
| needsMoreThoughts | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does an excellent job explaining behavioral traits. It describes the iterative nature ('Each thought can build on, question, or revise previous insights'), the adaptive process ('ready to adjust'), and specific behavioral expectations through the 11-point list (e.g., 'Mark thoughts that revise previous thinking', 'Only set next_thought_needed to false when truly done'). This provides substantial behavioral context beyond basic functionality.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, when to use, numbered guidelines) but is overly verbose at 11 numbered points. Some points could be consolidated (e.g., points about revision could be combined), and the 'When to use' section has redundant items ('Problems that require a multi-step solution' and 'Tasks that need to maintain context over multiple steps' are similar). The information is valuable but not optimally concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex tool with 9 parameters, 0% schema coverage, no annotations, and no output schema, the description provides excellent behavioral guidance but critically lacks parameter explanations. The purpose and usage guidelines are strong, but the agent would struggle to understand how to properly use the 9 parameters based solely on this description. The behavioral transparency helps but doesn't fully compensate for the parameter documentation gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage and 9 parameters, the description provides almost no parameter-specific information. While it mentions concepts like 'thought', 'revise previous thinking', and 'next_thought_needed' in the behavioral guidance, it doesn't explain what individual parameters mean, their relationships, or how they should be used together. The description fails to compensate for the complete lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'dynamic and reflective problem-solving through thoughts' and 'analyze problems through a flexible thinking process', which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like 'collaborativereasoning' or 'structuredargumentation', which might also involve multi-step reasoning processes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides an explicit 'When to use this tool' section with 7 specific scenarios (e.g., 'Breaking down complex problems into steps', 'Planning and design with room for revision'), giving clear guidance on appropriate contexts. It doesn't mention when NOT to use it or name specific alternatives, but the detailed usage scenarios provide strong directional guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
A detailed tool for systematic dialectical reasoning and argument analysis. This tool helps analyze complex questions through formal argumentation structures. It facilitates the creation, critique, and synthesis of competing arguments.
| Name | Required | Description | Default |
|---|---|---|---|
| claim | Yes | ||
| premises | Yes | ||
| conclusion | Yes | ||
| argumentId | No | Optional unique identifier for this argument | |
| argumentType | Yes | ||
| confidence | Yes | Confidence level in this argument (0.0-1.0) | |
| respondsTo | No | ID of the argument this directly responds to | |
| supports | No | IDs of arguments this supports | |
| contradicts | No | IDs of arguments this contradicts | |
| strengths | No | Notable strong points of the argument | |
| weaknesses | No | Notable weak points of the argument | |
| nextArgumentNeeded | Yes | Whether another argument is needed in the dialectic | |
| suggestedNextTypes | No | Suggested types for the next argument |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the tool's function but lacks critical details: it doesn't specify if this is a read-only or mutating operation, what the output looks like (e.g., structured analysis or plain text), or any constraints like rate limits or authentication needs. For a tool with 13 parameters and no output schema, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in three sentences, front-loaded with the core purpose. Each sentence adds value: the first defines the tool, the second specifies its application, and the third outlines key actions. There's no wasted text, though it could be slightly more detailed without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (13 parameters, no output schema, no annotations), the description is inadequate. It doesn't explain the output format, behavioral traits, or how parameters interact in practice. For a tool designed for 'systematic dialectical reasoning,' more context on expected inputs and results is needed to guide the agent effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 69%, meaning most parameters have some documentation in the schema itself. The description adds little beyond this, as it doesn't explain parameter meanings, relationships (e.g., how 'claim' and 'conclusion' differ), or usage examples. Given the high coverage, the baseline is 3, but the description fails to compensate for the 31% gap or provide deeper insights.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'systematic dialectical reasoning and argument analysis' with specific verbs like 'analyze,' 'create,' 'critique,' and 'synthesize.' It distinguishes the tool by focusing on formal argumentation structures, which differentiates it from sibling tools like 'collaborativereasoning' or 'scientificmethod.' However, it doesn't explicitly contrast with all siblings, keeping it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions analyzing 'complex questions' but gives no context for choosing this over sibling tools like 'decisionframework' or 'mentalmodel.' There are no exclusions, prerequisites, or named alternatives, leaving the agent with minimal usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
A tool for visual thinking, problem-solving, and communication. This tool enables models to create, manipulate, and interpret diagrams, graphs, and other visual representations. It supports various visual elements and operations to facilitate insight generation and hypothesis testing.
| Name | Required | Description | Default |
|---|---|---|---|
| operation | Yes | ||
| elements | No | ||
| transformationType | No | ||
| diagramId | Yes | ||
| diagramType | Yes | ||
| iteration | Yes | ||
| observation | No | ||
| insight | No | ||
| hypothesis | No | ||
| nextOperationNeeded | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'create, manipulate, and interpret' operations and 'facilitate insight generation and hypothesis testing,' but doesn't specify what happens during these operations—whether they're read-only, destructive, require specific permissions, have side effects, or produce outputs. For a tool with 10 parameters including operations like 'delete' and 'transform,' this lack of behavioral detail is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with three sentences that avoid redundancy. It's front-loaded with the core purpose ('A tool for visual thinking, problem-solving, and communication'), followed by capabilities and benefits. There's no wasted text, though the abstract nature limits its utility. The structure is clear but could be more actionable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (10 parameters, 5 required, multiple enums) and the absence of both annotations and an output schema, the description is inadequate. It doesn't explain what the tool returns, how errors are handled, or the scope of operations. For a multi-functional tool with parameters like 'observation' and 'hypothesis,' more context is needed to understand its complete behavior and outputs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning none of the 10 parameters have descriptions in the schema. The tool description doesn't explain any parameters—it doesn't mention 'operation,' 'elements,' 'diagramId,' or other key inputs. While it implies operations like 'create' and 'manipulate,' it doesn't map these to specific parameters or provide usage examples. The description fails to compensate for the complete lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool is for 'visual thinking, problem-solving, and communication' and enables 'create, manipulate, and interpret diagrams, graphs, and other visual representations.' This is vague and abstract rather than specific about what the tool actually does. It doesn't clearly distinguish from sibling tools like 'designpattern' or 'mentalmodel' that might also involve visual elements. The purpose is described at a conceptual level rather than as a concrete operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any specific contexts, prerequisites, or exclusions. Given the sibling tools include various reasoning approaches (e.g., 'collaborativereasoning', 'scientificmethod'), there's no indication of when visual reasoning is preferred over other methods. The description is completely silent on usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.1.2collaborativereasoningdebuggingapproachdecisionframeworkdesignpatternmentalmodelmetacognitivemonitoringprogrammingparadigmscientificmethodsequentialthinkingstructuredargumentationvisualreasoningScored across 11 tools
The tools have overlapping purposes in problem-solving and reasoning, which could cause confusion. For example, 'collaborativereasoning', 'decisionframework', 'mentalmodel', and 'structuredargumentation' all involve structured analysis approaches, making it difficult for an agent to choose the most appropriate one. However, the detailed descriptions help differentiate them to some extent by emphasizing specific aspects like collaboration, decision-making, mental models, or argumentation.
The naming is mostly consistent with a clear pattern of using descriptive, compound words (e.g., 'collaborativereasoning', 'metacognitivemonitoring'). All tools follow a similar style without mixing conventions like camelCase or snake_case. The only minor deviation is 'sequentialthinking', which uses a single word instead of a compound, but it still fits the overall readable and predictable naming scheme.
With 11 tools, the count is reasonable for a server focused on reasoning and problem-solving methodologies. It covers a broad range of approaches without being overwhelming. However, it might be slightly heavy as some tools could be consolidated (e.g., overlapping reasoning tools), but overall, it's well-scoped for the domain of structured thinking and analysis.
The tool set provides comprehensive coverage of reasoning and problem-solving domains, including collaboration, debugging, decision-making, design, mental models, metacognition, programming paradigms, scientific methods, sequential thinking, argumentation, and visual reasoning. There are no obvious major gaps, as it spans from high-level frameworks to specific techniques. Minor gaps might exist in more niche areas, but agents can likely work around them given the breadth of tools available.
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