Click on "Install 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., "@Pathfinder MCP Server start research for implementing user authentication"
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.
A FastMCP server implementing a three-phase gated workflow: Research → Plan → Implement (RPI).
Pathfinder enforces intentional context compaction and explicit human approval at each phase transition. It helps maintain focus during complex coding tasks by:
Research Phase: Gather information, explore codebase, document findings
Plan Phase: Create structured implementation plan with explicit phases
Implement Phase: Execute plan phases with progress tracking
Related MCP server: mcp-micromanage
# No installation needed - use uvx to run directly
uvx pathfinder-mcp# Clone the repository
git clone <repository-url>
cd pathfinder-mcp
# Install dependencies
uv sync
# Install as a global uv tool (editable mode)
uv tool install --editable .
# Install dev dependencies (optional)
uv sync --devWhat does uv tool install --editable do?
Installs the command globally (available system-wide)
Links to your source code (changes take effect immediately)
Isolated from other projects
No need for complex paths in MCP configs
Variable | Default | Description |
|
| Session storage path |
|
| Transport: |
|
| SSE server host |
|
| SSE server port |
|
| Max context tokens |
For Published Package (Recommended):
{
"mcpServers": {
"pathfinder": {
"command": "uvx",
"args": ["pathfinder-mcp"]
}
}
}For Local Development (After uv tool install --editable .):
{
"mcpServers": {
"pathfinder": {
"command": "pathfinder-mcp",
"env": {
"PATHFINDER_SESSIONS_DIR": "/Users/yourusername/.pathfinder-sessions"
}
}
}
}SSE Transport (Remote Server):
{
"mcpServers": {
"pathfinder": {
"command": "pathfinder-mcp",
"env": {
"PATHFINDER_TRANSPORT": "sse",
"PATHFINDER_HOST": "0.0.0.0",
"PATHFINDER_PORT": "8080"
}
}
}
}Note: Use absolute paths in environment variables (e.g.,
/Users/username/...instead of~). The~expansion may not work reliably in all MCP clients.
Tool | Phase | Description |
| Any | Server health and context status |
| - | Initialize research phase |
| Research | Save research findings |
| Research → Plan | Transition to planning (requires approval) |
| Plan | Save implementation plan |
| Plan/Implement | Execute implementation phase |
| Any | Compress session context |
URI | Description |
| Research template |
| Plan template (Cursor format) |
| Implementation checklist |
| Progress tracking template |
Prompt | Description |
| Create plan from research |
| Generate compressed summary |
| Restore session context |
1. start_research("Build auth system")
└─> Creates session, research.md
2. save_research(findings) [repeat as needed]
└─> Appends to research.md
3. start_plan() [asks for confirmation]
└─> Creates plan.md template, transitions to PLAN
4. save_plan(plan_content)
└─> Validates & saves plan
5. implement_phase() [asks for confirmation]
└─> Executes phase, updates progress.md
6. compact_context() [when utilization >60%]
└─> Creates session_summary.md~/.pathfinder-sessions/
{session_id}/
research.md # Research findings
plan.md # Implementation plan
progress.md # Implementation progress
session_state.json # Session snapshot
session_summary.md # Compacted summarysrc/pathfinder_mcp/
├── server.py # FastMCP server + tools
├── config.py # Environment configuration
├── state.py # Phase state machine
├── session.py # Session persistence
├── artifacts.py # Markdown file writer
├── context.py # Token monitoring
├── logger.py # Structured logging
├── errors.py # Error handling
├── handlers/ # Modular phase handlers
│ ├── base.py # BaseHandler ABC
│ ├── research.py # ResearchHandler
│ ├── plan.py # PlanHandler
│ └── implement.py # ImplementHandler
└── tools/ # Tool implementations
├── research.py
├── plan.py
├── implement.py
└── compact.pyUse just to run common development tasks:
# See all available recipes
just --list
# Run tests
just test
# Run tests with coverage report
just test-coverage
# Lint & format code
just check
# Fix linting issues
just lint-fix
# Run the server locally
just run
# Build the package
just buildIf you prefer to run commands directly:
# Run tests
uv run pytest
# Run tests with coverage
uv run pytest --cov=pathfinder_mcp
# Lint & format
uv run ruff check src/ tests/
uv run ruff format src/ tests/
# Run server locally
uv run pathfinder-mcpSetup automatic linting and formatting on every commit:
# Install pre-commit hooks (one-time)
just setup-hooks
# Or manually:
uv run pre-commit install
# Run pre-commit on all files
just pre-commit-all
# Pre-commit will automatically run on `git commit`Pathfinder tracks token usage and provides warnings:
60% utilization: Suggests compaction
70% utilization: Warns strongly
compact_context: Resets by creating summary
MIT
Compress session context into summary artifacts.
Use when context utilization exceeds 60%.
Args: session_id: Session ID
| Name | Required | Description | Default |
|---|---|---|---|
| session_id | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool compresses context into 'summary artifacts,' implying a read/write operation that transforms data, but doesn't disclose behavioral traits like whether it's destructive, requires specific permissions, has rate limits, or what 'summary artifacts' entail. This leaves significant gaps for a tool that modifies session 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 front-loaded: the first sentence states the purpose, followed by usage guidelines and parameter info. There's no wasted text, and it's structured clearly with bullet points for Args. It could be slightly more concise by integrating the Args into a single sentence, 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 tool's complexity (compression operation), no annotations, and an output schema exists (which should cover return values), the description is minimally complete. It explains what the tool does and when to use it, but lacks details on behavioral aspects and parameter nuances. For a context-manipulation tool, this is adequate but leaves room for improvement.
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?
The description adds minimal parameter semantics: it lists 'session_id: Session ID' under Args, which matches the single parameter in the schema. With 0% schema description coverage, the description doesn't compensate by explaining format, constraints, or examples. However, since there's only one parameter and it's straightforward, the baseline is met but not exceeded.
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: 'Compress session context into summary artifacts.' It specifies the verb ('compress') and resource ('session context'), and distinguishes it from siblings like health_check or save_plan. However, it doesn't explicitly differentiate from all siblings (e.g., implement_phase might also process context), so it's not a perfect 5.
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 clear usage guidance: 'Use when context utilization exceeds 60%'—this gives a specific threshold for when to invoke the tool. It doesn't mention alternatives or exclusions, but the context is sufficiently clear for an AI agent to decide when to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check server health and return status.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
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 the tool checks health and returns status, but doesn't specify what 'status' includes (e.g., uptime, metrics, errors), whether it's a read-only operation, or any side effects like logging or performance impact. For a tool with no annotations, this leaves significant gaps in understanding its 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 concise and front-loaded with a single sentence ('Check server health and return status.'), which efficiently states the core purpose without unnecessary words. However, it could be slightly more structured by elaborating on what 'health' means, but it earns high marks for brevity and clarity.
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 has no parameters, an output schema exists (which should cover return values), and no annotations, the description is minimally adequate. It states what the tool does but lacks depth on behavioral aspects like what 'health' entails or usage context. For a simple health check tool, it's passable but could be more informative to fully guide 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?
The tool has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics, and it correctly doesn't mention any. Baseline is 4 for zero parameters, as there's nothing to compensate for.
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's purpose ('Check server health and return status'), which is clear but vague. It specifies the verb ('Check') and resource ('server health'), but doesn't distinguish it from potential sibling tools like 'compact_context' or 'save_plan'. The purpose is understandable but lacks specificity about what 'health' entails.
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. The description doesn't mention context, prerequisites, or exclusions, and with sibling tools like 'start_plan' or 'save_research', there's no indication of how this tool fits into workflows. It's a basic statement without usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Execute an implementation phase from the plan.
Args: session_id: Session ID phase_number: Specific phase to execute (defaults to next uncompleted)
| Name | Required | Description | Default |
|---|---|---|---|
| session_id | Yes | ||
| phase_number | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
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 states the tool 'Execute[s] an implementation phase,' implying a mutation or action, but doesn't clarify what 'execute' entails—whether it's a read-only simulation, a destructive change, requires specific permissions, or has side effects like rate limits. This leaves critical behavioral traits unspecified.
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 core purpose stated in the first sentence and parameter details in a structured 'Args:' section. Every sentence adds value without redundancy, making it efficient and easy to parse.
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 (involving plan execution with 2 parameters), no annotations, and an output schema present (which reduces the need to describe return values), the description is minimally adequate. It covers the basic purpose and parameters but lacks behavioral context and usage guidelines, leaving gaps in completeness.
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?
The description adds meaningful semantics beyond the input schema, which has 0% description coverage. It explains that 'session_id' is a 'Session ID' and 'phase_number' is a 'Specific phase to execute (defaults to next uncompleted),' clarifying the purpose and default behavior of parameters that the schema only defines as string/integer/null types.
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 with a specific verb ('Execute') and resource ('an implementation phase from the plan'), making it immediately understandable. However, it doesn't explicitly differentiate this tool from its siblings like 'start_plan' or 'save_plan', which might involve similar plan-related operations.
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 prerequisites (e.g., whether a plan must exist first), exclusions, or relationships to sibling tools like 'start_plan' or 'save_plan', leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Save implementation plan.
Validates plan follows Cursor plan format with YAML frontmatter.
Args: session_id: Session ID plan_content: Plan content in Cursor plan format
| Name | Required | Description | Default |
|---|---|---|---|
| session_id | Yes | ||
| plan_content | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
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 validation of the plan format, which is a useful behavioral trait, but fails to cover critical aspects: whether this is a read-only or write operation (implied write from 'save'), what happens on success/failure (e.g., error messages, storage location), permissions required, or side effects. For a tool with no annotation coverage, this leaves significant gaps in understanding its 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, starting with the core purpose and validation details, followed by parameter explanations. Every sentence adds value: the first defines the action, the second specifies validation, and the args section clarifies parameters. Minor improvement could come from integrating parameter details more seamlessly, but overall it's efficient with zero waste.
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 has an output schema (which likely covers return values), no annotations, and low schema description coverage (0%), the description is moderately complete. It covers the purpose and parameters adequately but lacks behavioral details (e.g., error handling, side effects) and usage guidelines. For a save operation with validation, more context on outcomes and constraints would enhance completeness.
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?
The description adds meaningful context for both parameters beyond the input schema, which has 0% description coverage. It explains that 'session_id' is a Session ID and 'plan_content' is Plan content in Cursor plan format, clarifying their roles in the saving process. However, it doesn't detail format specifics (e.g., what constitutes a valid session_id or the exact structure of Cursor plan format), keeping it from a perfect 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: 'Save implementation plan' with the specific action of validating that the plan follows the Cursor plan format with YAML frontmatter. This distinguishes it from sibling tools like 'start_plan' (which likely initiates planning) and 'implement_phase' (which likely executes phases). However, it doesn't explicitly contrast with 'save_research' (which might save research data), leaving some sibling differentiation incomplete.
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 prerequisites (e.g., whether a plan must be started first), exclusions, or comparisons to siblings like 'start_plan' or 'save_research'. The agent must infer usage from the purpose alone, which is insufficient for optimal tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Save research findings to the session.
Args: session_id: Session ID findings: Research findings to save
| Name | Required | Description | Default |
|---|---|---|---|
| session_id | Yes | ||
| findings | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the tool saves findings but doesn't disclose behavioral traits like whether this overwrites existing data, requires specific permissions, has rate limits, or what the output looks like. For a mutation tool with zero annotation coverage, this 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 sized with two sentences: one stating the purpose and another listing parameters. It's front-loaded with the core functionality, though the parameter section could be more integrated. There's minimal waste, but it could be slightly more structured.
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 has an output schema (which handles return values) and no annotations, the description covers the basic purpose and parameters. However, for a mutation tool with 2 parameters and 0% schema coverage, it should provide more context on behavior and usage compared to siblings. It's minimally adequate but has clear gaps.
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. It lists both parameters ('session_id' and 'findings') with brief explanations, but doesn't add meaningful semantics like format expectations, constraints, or examples. The description partially documents the parameters but doesn't fully compensate for the schema's lack of descriptions.
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 action ('save') and resource ('research findings') with the destination ('to the session'). It's specific about what the tool does but doesn't differentiate from sibling tools like 'save_plan' or 'start_research' which might handle similar resources.
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 like 'save_plan' or 'start_research'. It mentions the session context but doesn't specify prerequisites, exclusions, or comparison with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Transition from research to plan phase.
Creates plan.md template. Requires research to be complete.
Args: session_id: Session ID
| Name | Required | Description | Default |
|---|---|---|---|
| session_id | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool creates a file (plan.md template) and has a prerequisite (research completion), but lacks details on permissions, error handling, or what 'transition' entails behaviorally. It doesn't contradict annotations, but offers limited behavioral insight.
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: the first sentence states the core purpose, followed by key details and parameter info. Every sentence earns its place with no wasted words, making it efficient and well-structured.
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 moderate complexity (phase transition with a prerequisite), no annotations, and an output schema present, the description is mostly complete. It covers purpose, usage context, and parameter semantics, but could benefit from more behavioral details, though the output schema reduces the need to explain return values.
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?
The description adds meaning beyond the input schema by explaining that 'session_id' is a 'Session ID', though this is minimal. With 0% schema description coverage and only 1 parameter, the baseline is high, and the description compensates adequately by clarifying the parameter's role.
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: 'Transition from research to plan phase' and 'Creates plan.md template'. It specifies both the action (transition/creates) and the resource (plan.md template), though it doesn't explicitly differentiate from sibling tools like 'save_plan' or 'start_research'.
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 clear context for usage: 'Requires research to be complete' indicates a prerequisite. However, it doesn't explicitly state when not to use this tool or name alternatives among siblings, such as when to use 'save_plan' instead.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Start a new research session.
Args: task_description: Description of the task to research session_id: Optional session ID (auto-generated if not provided)
| Name | Required | Description | Default |
|---|---|---|---|
| task_description | Yes | ||
| session_id | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
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 states the tool starts a research session but doesn't explain what that entails (e.g., does it create resources, require authentication, have side effects, or return a session object?). This is inadequate for a tool that likely initiates a stateful process.
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 core purpose in the first sentence. The parameter explanations are brief but clear, with no wasted words. It could be slightly more structured but is 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 tool likely initiates a research workflow (implied by sibling tools), no annotations, and an output schema exists (which reduces need to describe returns), the description is minimally adequate. However, it lacks context on behavioral traits and integration with other tools, leaving gaps in understanding the tool's role.
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?
The description adds parameter semantics beyond the schema: it clarifies that 'task_description' describes the research task and 'session_id' is optional and auto-generated if not provided. However, with 0% schema description coverage, this only partially compensates for the two parameters, leaving details like format constraints unexplained.
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 action ('Start a new research session') and the resource ('research session'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'start_plan' or 'save_research' which appear related to the same workflow, so it doesn't reach the highest 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 like 'start_plan' or 'save_research'. There's no mention of prerequisites, typical workflow context, or exclusions, leaving the agent with minimal usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Most tools have clearly distinct purposes: health_check, compact_context, save_research, start_research, start_plan, save_plan, and implement_phase each target different phases or actions in a workflow. However, save_research and save_plan could be slightly confused as both involve saving content, but their descriptions clarify they handle different types of data (research findings vs. implementation plans).
The naming follows a consistent verb_noun pattern throughout (e.g., compact_context, health_check, implement_phase, save_plan, save_research, start_plan, start_research), which is predictable and readable. There are no deviations in style or convention, making it easy for agents to understand the action each tool performs.
With 7 tools, the count is well-scoped for a server focused on managing research and implementation workflows. Each tool appears to earn its place by covering distinct steps in the process, from starting research to executing phases, without being overly sparse or bloated.
The tool set covers key phases like research, planning, and implementation, but there are notable gaps. For example, there are no tools for updating or deleting saved plans or research, and operations like reviewing or listing existing sessions are missing. This could lead to dead ends for agents trying to manage ongoing work comprehensively.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Adaptive plan/build/review cycles for AI coding assistants, persisted across sessions.
Coordinate coding agents through MCP using existing AI plans, saved work, and independent checks.
Preventive human-approval write-gate for AI agents: writes commit only after a human approves.
Deterministic AI code review, with an audit record. Governance inside the agent loop.
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