ai.plith/plith

Official15 tools

by plith

AI agent infrastructure: dedup, cost prediction, validation, governance, failure intelligence.

MCP Server: ai.plith/plith

The MCP server ai.plith/plith provides AI agent infrastructure centered on dedup, cost prediction, validation, governance, and failure intelligence. Its purpose is to support agent workflows with tools count: 15 available capabilities.

🛠️ Key Features

Deduplication
Cost prediction
Validation
Governance
Failure intelligence

🚀 Use Cases

Reducing duplicate work in agent runs
Predicting costs for AI agent operations
Validating agent outputs or inputs
Applying governance controls during execution
Understanding and handling failure conditions

⚡ Developer Benefits

15 tools to integrate infrastructure functions
Clear support for validation and governance needs
Failure intelligence to inform recovery or troubleshooting

⚠️ Limitations

Provided description does not include specific protocol endpoints, supported models, or tool names.

Captured live from the server via tools/list.

dedupq_check

Before executing any LLM task, check if an identical or semantically similar task has already been completed. Returns cached result on hit, saving one LLM call. On a miss, execute your task and call dedupq_complete to cache the result for future hits. Costs 1 credit.

Parameters4

content	string	required	The task content to check for duplicates. This is hashed and embedded for matching.
task_id	string	optional	Optional caller task ID for tracing and cross-referencing with BurnRate.
hash_only	boolean	optional	If true, skip vector similarity search and use exact hash matching only. Default: false.
similarity_threshold	number	optional	Cosine similarity threshold for semantic matching, 0.0 to 1.0. Default: 0.80.

Raw schema

{
  "type": "object",
  "properties": {
    "content": {
      "type": "string",
      "description": "The task content to check for duplicates. This is hashed and embedded for matching."
    },
    "task_id": {
      "type": "string",
      "description": "Optional caller task ID for tracing and cross-referencing with BurnRate."
    },
    "hash_only": {
      "type": "boolean",
      "description": "If true, skip vector similarity search and use exact hash matching only. Default: false."
    },
    "similarity_threshold": {
      "type": "number",
      "description": "Cosine similarity threshold for semantic matching, 0.0 to 1.0. Default: 0.80."
    }
  },
  "required": [
    "content"
  ],
  "additionalProperties": false
}

dedupq_complete

After executing a task, store the result so future identical or similar tasks return a cache hit via dedupq_check. Costs 2 credits.

Parameters4

content	string	required	Original task content. Used to compute hash and embedding for future matching.
result	any	required	The task result to cache. Can be any JSON value.
task_id	string	optional	Optional task ID. Used as the database row ID if provided.
hash_only	boolean	optional	If true, skip embedding generation. Default: false.

Raw schema

{
  "type": "object",
  "properties": {
    "content": {
      "type": "string",
      "description": "Original task content. Used to compute hash and embedding for future matching."
    },
    "result": {
      "description": "The task result to cache. Can be any JSON value."
    },
    "task_id": {
      "type": "string",
      "description": "Optional task ID. Used as the database row ID if provided."
    },
    "hash_only": {
      "type": "boolean",
      "description": "If true, skip embedding generation. Default: false."
    }
  },
  "required": [
    "content",
    "result"
  ],
  "additionalProperties": false
}

burnrate_estimate

Before executing a multi-step agent plan, estimate the total LLM cost. Returns per-step breakdown and optimization suggestions. If the estimate exceeds your budget, pipe the same plan into burnrate_optimize. Costs 1 credit.

Parameters1

plan

array

required

Array of plan steps with provider, model, and token estimates.

Raw schema

{
  "type": "object",
  "properties": {
    "plan": {
      "type": "array",
      "description": "Array of plan steps with provider, model, and token estimates.",
      "items": {
        "type": "object",
        "properties": {
          "step": {
            "type": "string",
            "description": "Step identifier — string label or number."
          },
          "provider": {
            "type": "string",
            "description": "LLM provider: anthropic, openai, google, etc."
          },
          "model": {
            "type": "string",
            "description": "Model name: claude-sonnet-4-6, gpt-4o, etc."
          },
          "estimated_input_tokens": {
            "type": "number",
            "description": "Estimated prompt token count."
          },
          "estimated_output_tokens": {
            "type": "number",
            "description": "Estimated completion token count."
          },
          "cache_read_tokens": {
            "type": "number",
            "description": "Optional. Cached prompt tokens for providers with prompt caching."
          },
          "search_calls": {
            "type": "number",
            "description": "Optional. Number of grounding/search calls (e.g. Gemini Search)."
          }
        },
        "required": [
          "step",
          "provider",
          "model",
          "estimated_input_tokens",
          "estimated_output_tokens"
        ]
      }
    }
  },
  "required": [
    "plan"
  ],
  "additionalProperties": false
}

burnrate_track

Log the actual cost of an LLM call after execution. Call this after every LLM request to build calibration data that improves burnrate_estimate accuracy over time. Free — no credits charged. Returns the recorded cost entry with computed margin versus the prior estimate when one exists for this model and token range.

Parameters6

provider	string	required	LLM provider identifier. Supported: anthropic, openai, google, mistral, cohere, deepseek, together, fireworks, groq. Must match the provider of the model used.
model	string	required	Model identifier as returned by the provider. Examples: claude-sonnet-4-6, gpt-4o, gemini-2.0-flash, mistral-large-latest. Unknown models are accepted but cost may show as $0.
input_tokens	number	required	Actual prompt tokens used. Must be >= 0.
output_tokens	number	required	Actual completion tokens used. Must be >= 0.
task_id	string	optional	Optional task ID for cross-referencing spend with DedupQ deduplication results. Use the same task_id passed to dedupq_check to link cost tracking with deduplication.
cache_read_tokens	number	optional	Optional. Cache-read tokens.

Raw schema

{
  "type": "object",
  "properties": {
    "provider": {
      "type": "string",
      "description": "LLM provider identifier. Supported: anthropic, openai, google, mistral, cohere, deepseek, together, fireworks, groq. Must match the provider of the model used."
    },
    "model": {
      "type": "string",
      "description": "Model identifier as returned by the provider. Examples: claude-sonnet-4-6, gpt-4o, gemini-2.0-flash, mistral-large-latest. Unknown models are accepted but cost may show as $0."
    },
    "input_tokens": {
      "type": "number",
      "description": "Actual prompt tokens used. Must be >= 0."
    },
    "output_tokens": {
      "type": "number",
      "description": "Actual completion tokens used. Must be >= 0."
    },
    "task_id": {
      "type": "string",
      "description": "Optional task ID for cross-referencing spend with DedupQ deduplication results. Use the same task_id passed to dedupq_check to link cost tracking with deduplication."
    },
    "cache_read_tokens": {
      "type": "number",
      "description": "Optional. Cache-read tokens."
    }
  },
  "required": [
    "provider",
    "model",
    "input_tokens",
    "output_tokens"
  ],
  "additionalProperties": false
}

burnrate_optimize

Get a cheaper equivalent plan by substituting models with lower-cost alternatives. Call after burnrate_estimate if the estimated cost exceeds your budget. Returns the optimized plan with substituted models, new per-step costs, total savings, and whether the target_budget is met. Optionally set target_budget to constrain the optimization. Costs 1 credit.

Parameters2

plan	array	required	Array of plan steps. Same schema as burnrate_estimate: each step needs step, provider, model, estimated_input_tokens, estimated_output_tokens.
target_budget	number	optional	Optional. Target total cost in USD.

Raw schema

{
  "type": "object",
  "properties": {
    "plan": {
      "type": "array",
      "description": "Array of plan steps. Same schema as burnrate_estimate: each step needs step, provider, model, estimated_input_tokens, estimated_output_tokens.",
      "items": {
        "type": "object",
        "properties": {
          "step": {
            "type": "string",
            "description": "Step identifier — string label or number."
          },
          "provider": {
            "type": "string",
            "description": "LLM provider."
          },
          "model": {
            "type": "string",
            "description": "Model name."
          },
          "estimated_input_tokens": {
            "type": "number",
            "description": "Estimated input tokens."
          },
          "estimated_output_tokens": {
            "type": "number",
            "description": "Estimated output tokens."
          }
        },
        "required": [
          "step",
          "provider",
          "model",
          "estimated_input_tokens",
          "estimated_output_tokens"
        ]
      }
    },
    "target_budget": {
      "type": "number",
      "description": "Optional. Target total cost in USD."
    }
  },
  "required": [
    "plan"
  ],
  "additionalProperties": false
}

burnrate_budget

Get today's tracked LLM spend, per-model breakdown, projection, and budget alerts. Free — no credits charged.

Parameters1

daily_limit

number

optional

Optional. Daily budget in USD (e.g., 10.0 for a $10/day cap). Enables budget alerts and remaining-balance calculation.

Raw schema

{
  "type": "object",
  "properties": {
    "daily_limit": {
      "type": "number",
      "description": "Optional. Daily budget in USD (e.g., 10.0 for a $10/day cap). Enables budget alerts and remaining-balance calculation."
    }
  },
  "additionalProperties": false
}

qualitygate_validate

After your agent generates output, validate it against your rules before shipping. Runs deterministic checks (regex, JSON schema, syntax) plus optional LLM-powered tone and factual analysis. Returns a structured verdict (pass, warn, or fail) with a 0-100 score and per-check issue details. Use qualitygate_trends to spot recurring failure patterns over time. Variable cost: 1 credit per deterministic check, 8 credits per LLM check.

Parameters7

output	string	required	The agent output text to validate.
directives	array	optional	Directive objects. Types: must_include, must_not_include, must_match, must_not_match, must_contain, must_not_contain, min_length, max_length.
schema	object	optional	JSON Schema to validate output against.
language	string	optional	Code language for syntax check: json, python, javascript, typescript.
check_types	array	optional	Checks to run. Auto-inferred if omitted.
override	boolean	optional	Force pass. Requires override_reason.
override_reason	string	optional	Required when override is true.

Raw schema

{
  "type": "object",
  "properties": {
    "output": {
      "type": "string",
      "description": "The agent output text to validate."
    },
    "directives": {
      "type": "array",
      "description": "Directive objects. Types: must_include, must_not_include, must_match, must_not_match, must_contain, must_not_contain, min_length, max_length.",
      "items": {
        "type": "object",
        "properties": {
          "type": {
            "type": "string",
            "description": "Directive type."
          },
          "value": {
            "type": "string",
            "description": "Directive value — string pattern, regex, keyword array (as comma-separated string), or number (as string). Interpreted based on directive type."
          },
          "name": {
            "type": "string",
            "description": "Optional directive name."
          }
        },
        "required": [
          "type",
          "value"
        ]
      }
    },
    "schema": {
      "type": "object",
      "description": "JSON Schema to validate output against."
    },
    "language": {
      "type": "string",
      "description": "Code language for syntax check: json, python, javascript, typescript."
    },
    "check_types": {
      "type": "array",
      "items": {
        "type": "string",
        "enum": [
          "directive_compliance",
          "schema_validation",
          "code_syntax",
          "tone",
          "factual_claims"
        ]
      },
      "description": "Checks to run. Auto-inferred if omitted."
    },
    "override": {
      "type": "boolean",
      "description": "Force pass. Requires override_reason."
    },
    "override_reason": {
      "type": "string",
      "description": "Required when override is true."
    }
  },
  "required": [
    "output"
  ],
  "additionalProperties": false
}

guardrail_check

Evaluate a proposed agent action against your governance policies. Returns allow or deny with the matched policy reason. Requires at least one active policy created via guardrail_create_policy. Deterministic rule evaluation — no LLM. Costs 1 credit.

Parameters2

agent_id	string	required	Agent identifier.
proposed_action	object	required	Action to evaluate. Must contain a 'type' field. Example: {"type": "http_request", "url": "https://external.example.com"} or {"type": "file_write", "path": "/etc/config"}.

Raw schema

{
  "type": "object",
  "properties": {
    "agent_id": {
      "type": "string",
      "description": "Agent identifier."
    },
    "proposed_action": {
      "type": "object",
      "description": "Action to evaluate. Must contain a 'type' field. Example: {\"type\": \"http_request\", \"url\": \"https://external.example.com\"} or {\"type\": \"file_write\", \"path\": \"/etc/config\"}.",
      "properties": {
        "type": {
          "type": "string",
          "description": "Action type: http_request, delete_file, send_email, etc."
        }
      },
      "required": [
        "type"
      ]
    }
  },
  "required": [
    "agent_id",
    "proposed_action"
  ],
  "additionalProperties": false
}

guardrail_create_policy

Create a persistent governance policy that guardrail_check evaluates on every subsequent call. Define rules using and/or/not operators over action types, resource patterns, and budget thresholds. Call this before using guardrail_check — checks require at least one active policy. Policies persist until explicitly deleted. Duplicate policy names return an error. Returns the created policy with its ID and active status.

Parameters5

name	string	required	Unique policy name per org. Examples: 'no-delete-in-prod', 'budget-cap-50', 'pii-block'.
description	string	optional	Optional human-readable summary of what this policy enforces. Returned in guardrail_check responses and guardrail_list_policies output for auditability.
rules	array	required	Array of rule objects evaluated against the proposed_action in guardrail_check. Leaf operators: eq, starts_with, contains, gt, lt (compare field to value). Compound operators: and, or, not (nest sub-rules in a rules array). Example: [{operator:'eq', field:'type', value:'file_write'}] blocks all file writes. Nested example: [{operator:'and', rules:[{operator:'eq',field:'type',value:'api_call'},{operator:'contains',field:'url',value:'prod'}]}] blocks prod API calls.
action_types	array	optional	Optional. Restrict this policy to only evaluate when proposed_action.type matches one of these values. Examples: ['file_write', 'api_call', 'db_delete']. Omit to apply the policy to all action types regardless of type field.
priority	number	optional	Optional. Evaluation order. Default: 0.

Raw schema

{
  "type": "object",
  "properties": {
    "name": {
      "type": "string",
      "description": "Unique policy name per org. Examples: 'no-delete-in-prod', 'budget-cap-50', 'pii-block'."
    },
    "description": {
      "type": "string",
      "description": "Optional human-readable summary of what this policy enforces. Returned in guardrail_check responses and guardrail_list_policies output for auditability."
    },
    "rules": {
      "type": "array",
      "description": "Array of rule objects evaluated against the proposed_action in guardrail_check. Leaf operators: eq, starts_with, contains, gt, lt (compare field to value). Compound operators: and, or, not (nest sub-rules in a rules array). Example: [{operator:'eq', field:'type', value:'file_write'}] blocks all file writes. Nested example: [{operator:'and', rules:[{operator:'eq',field:'type',value:'api_call'},{operator:'contains',field:'url',value:'prod'}]}] blocks prod API calls.",
      "items": {
        "type": "object",
        "properties": {
          "operator": {
            "type": "string",
            "description": "Rule operator: eq | starts_with | contains | gt | lt | and | or | not."
          },
          "field": {
            "type": "string",
            "description": "Field path on proposed_action (e.g. 'action_type', 'path', 'amount')."
          },
          "value": {
            "type": "string",
            "description": "Comparison value for leaf operators. String, number, or boolean as string."
          },
          "rules": {
            "type": "array",
            "description": "Nested rules for compound operators (and/or/not)."
          }
        },
        "required": [
          "operator"
        ]
      }
    },
    "action_types": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Optional. Restrict this policy to only evaluate when proposed_action.type matches one of these values. Examples: ['file_write', 'api_call', 'db_delete']. Omit to apply the policy to all action types regardless of type field."
    },
    "priority": {
      "type": "number",
      "description": "Optional. Evaluation order. Default: 0."
    }
  },
  "required": [
    "name",
    "rules"
  ],
  "additionalProperties": false
}

pitfalldb_query

Check for known failure patterns before executing a task type. Returns pitfalls with severity, fix suggestions, and confidence scores. After your agent runs, submit failures via pitfalldb_report so others benefit. Costs 2 credits.

Parameters3

task_type	string	required	Task category: code_generation, web_search, data_analysis, etc.
task_description	string	optional	Optional. Natural-language task description for semantic search.
filters	object	optional	Optional filters.

Raw schema

{
  "type": "object",
  "properties": {
    "task_type": {
      "type": "string",
      "description": "Task category: code_generation, web_search, data_analysis, etc."
    },
    "task_description": {
      "type": "string",
      "description": "Optional. Natural-language task description for semantic search."
    },
    "filters": {
      "type": "object",
      "description": "Optional filters.",
      "properties": {
        "language": {
          "type": "string",
          "description": "Programming language."
        },
        "framework": {
          "type": "string",
          "description": "Framework."
        },
        "provider": {
          "type": "string",
          "description": "LLM provider."
        }
      }
    }
  },
  "required": [
    "task_type"
  ],
  "additionalProperties": false
}

pitfalldb_report

Report an agent failure. PII-scrubbed before storage. Linked to existing pitfalls if similar. Free — no credits charged.

Parameters3

task_type	string	required	Task category.
task_description	string	required	Description of the failed task.
failure	object	required	Failure details.

Raw schema

{
  "type": "object",
  "properties": {
    "task_type": {
      "type": "string",
      "description": "Task category."
    },
    "task_description": {
      "type": "string",
      "description": "Description of the failed task."
    },
    "failure": {
      "type": "object",
      "description": "Failure details.",
      "properties": {
        "error_type": {
          "type": "string",
          "description": "Error category: tool_call_ignored, syntax_error, etc."
        },
        "error_message": {
          "type": "string",
          "description": "Error message (PII-scrubbed)."
        },
        "root_cause": {
          "type": "string",
          "description": "Root cause analysis (PII-scrubbed)."
        },
        "fix_applied": {
          "type": "string",
          "description": "Fix applied (PII-scrubbed)."
        },
        "fix_worked": {
          "type": "boolean",
          "description": "Whether the fix worked."
        },
        "language": {
          "type": "string",
          "description": "Programming language."
        },
        "framework": {
          "type": "string",
          "description": "Framework."
        },
        "provider": {
          "type": "string",
          "description": "LLM provider."
        }
      }
    }
  },
  "required": [
    "task_type",
    "task_description",
    "failure"
  ],
  "additionalProperties": false
}

rigor_plan

Before executing a complex task, get a structured workflow plan with per-step cost estimates. Classifies your task, selects the optimal framework sequence, and returns the full plan without executing anything. Free — no credits charged.

Parameters3

task_description	string	required	Natural language description of the task. Be specific — include what you want produced, constraints, and context. Example: 'Design a caching layer for our API gateway with Redis integration.'
task_type	string	optional	Optional hint to bypass automatic classification. Values: solution_design, requirements_analysis, code_implementation, code_review, bug_fix, root_cause_analysis, incident_response, deployment_execution, competitive_scan, financial_analysis, research_task, documentation, governance_change, compliance_audit, data_security_assessment, performance_optimization, user_story_definition, implementation_prompt_generation.
preferences	object	optional	Optional workflow preferences.

Raw schema

{
  "type": "object",
  "properties": {
    "task_description": {
      "type": "string",
      "description": "Natural language description of the task. Be specific — include what you want produced, constraints, and context. Example: 'Design a caching layer for our API gateway with Redis integration.'"
    },
    "task_type": {
      "type": "string",
      "description": "Optional hint to bypass automatic classification. Values: solution_design, requirements_analysis, code_implementation, code_review, bug_fix, root_cause_analysis, incident_response, deployment_execution, competitive_scan, financial_analysis, research_task, documentation, governance_change, compliance_audit, data_security_assessment, performance_optimization, user_story_definition, implementation_prompt_generation."
    },
    "preferences": {
      "type": "object",
      "description": "Optional workflow preferences.",
      "properties": {
        "rigor_level": {
          "type": "string",
          "enum": [
            "quick",
            "standard",
            "thorough"
          ],
          "description": "Review depth. Default auto-detected from task complexity."
        },
        "max_budget_usd": {
          "type": "number",
          "description": "Budget ceiling in USD. Triggers warning if plan exceeds this."
        },
        "require_approval": {
          "type": "boolean",
          "description": "Pause at pending_approval before the final step."
        },
        "approval_before_step": {
          "type": "array",
          "items": {
            "type": "number"
          },
          "description": "Zero-based step indices where approval gates are inserted."
        },
        "skip_frameworks": {
          "type": "array",
          "items": {
            "type": "string"
          },
          "description": "Framework names to exclude from the plan."
        },
        "only_frameworks": {
          "type": "array",
          "items": {
            "type": "string"
          },
          "description": "Restrict plan to only these frameworks (mutex with skip_frameworks)."
        },
        "add_frameworks": {
          "type": "array",
          "items": {
            "type": "string"
          },
          "description": "Inject additional frameworks into the plan."
        }
      }
    }
  },
  "required": [
    "task_description"
  ],
  "additionalProperties": false
}

rigor_execute

Execute a structured workflow end-to-end. Call rigor_plan first (free) to preview the step sequence and cost estimate before committing credits. Classifies the task, selects the optimal tool sequence, and executes each step with the right LLM model. Returns a complete deliverable — solution designs, competitive analyses, governance documents, and more. Supports SSE streaming for real-time progress, webhook callback, or polling.

Parameters5

task_description	string	required	Natural language description of the task. Be specific — include what you want produced, constraints, and context. Example: 'Design a caching layer for our API gateway with Redis integration.'
task_type	string	optional	Optional hint to bypass automatic classification. Values: solution_design, requirements_analysis, code_implementation, code_review, bug_fix, root_cause_analysis, incident_response, deployment_execution, competitive_scan, financial_analysis, research_task, documentation, governance_change, compliance_audit, data_security_assessment, performance_optimization, user_story_definition, implementation_prompt_generation.
delivery	object	optional	Delivery method. Default: polling (MCP clients typically can't handle SSE).
preferences	object	optional	Optional workflow preferences.
context	object	optional	Additional context for the workflow.

Raw schema

{
  "type": "object",
  "properties": {
    "task_description": {
      "type": "string",
      "description": "Natural language description of the task. Be specific — include what you want produced, constraints, and context. Example: 'Design a caching layer for our API gateway with Redis integration.'"
    },
    "task_type": {
      "type": "string",
      "description": "Optional hint to bypass automatic classification. Values: solution_design, requirements_analysis, code_implementation, code_review, bug_fix, root_cause_analysis, incident_response, deployment_execution, competitive_scan, financial_analysis, research_task, documentation, governance_change, compliance_audit, data_security_assessment, performance_optimization, user_story_definition, implementation_prompt_generation."
    },
    "delivery": {
      "type": "object",
      "description": "Delivery method. Default: polling (MCP clients typically can't handle SSE).",
      "properties": {
        "method": {
          "type": "string",
          "description": "sse | webhook | polling. Default for MCP: polling."
        },
        "webhook_url": {
          "type": "string",
          "description": "Required if method is webhook. Must be HTTPS."
        }
      }
    },
    "preferences": {
      "type": "object",
      "description": "Optional workflow preferences.",
      "properties": {
        "rigor_level": {
          "type": "string",
          "description": "quick | standard (default) | thorough. Controls analysis depth and cost."
        },
        "max_budget_usd": {
          "type": "number",
          "description": "Maximum budget in USD."
        }
      }
    },
    "context": {
      "type": "object",
      "description": "Additional context for the workflow.",
      "properties": {
        "additional_context": {
          "type": "string",
          "description": "Free-form context the workflow steps can reference."
        }
      }
    }
  },
  "required": [
    "task_description"
  ],
  "additionalProperties": false
}

rigor_status

Check the status of a running or completed Rigor workflow. Returns progress, step results, and the full deliverable when complete. Use after rigor_execute with polling delivery to retrieve results.

Parameters1

workflow_id

string

required

The workflow ID returned by rigor_execute (format: wr_xxx).

Raw schema

{
  "type": "object",
  "properties": {
    "workflow_id": {
      "type": "string",
      "description": "The workflow ID returned by rigor_execute (format: wr_xxx)."
    }
  },
  "required": [
    "workflow_id"
  ],
  "additionalProperties": false
}

rigor_workflows

List all Rigor workflows for your organization with filtering and pagination. Returns status, progress, capacity usage, and available actions per workflow. Use to monitor workflow state, understand concurrent limit usage, and identify stuck or completed workflows.

Parameters5

status	string	optional	Filter by status (comma-separated). Valid values: executing, step_executing, completed, failed, halted, pending_approval, cancelled. E.g. "halted,failed,pending_approval"
task_type	string	optional	Filter by classified task type
counts_toward_limit	string	optional	Filter to workflows counting toward the concurrent limit
limit	number	optional	Page size (default 20, max 100)
cursor	string	optional	Pagination cursor (created_at timestamp from previous page)

Raw schema

{
  "type": "object",
  "properties": {
    "status": {
      "type": "string",
      "description": "Filter by status (comma-separated). Valid values: executing, step_executing, completed, failed, halted, pending_approval, cancelled. E.g. \"halted,failed,pending_approval\""
    },
    "task_type": {
      "type": "string",
      "description": "Filter by classified task type"
    },
    "counts_toward_limit": {
      "type": "string",
      "enum": [
        "true",
        "false"
      ],
      "description": "Filter to workflows counting toward the concurrent limit"
    },
    "limit": {
      "type": "number",
      "description": "Page size (default 20, max 100)"
    },
    "cursor": {
      "type": "string",
      "description": "Pagination cursor (created_at timestamp from previous page)"
    }
  },
  "required": [],
  "additionalProperties": false
}

Install

Tools (15)

dedupq_checkdedupq_completeburnrate_estimateburnrate_trackburnrate_optimizeburnrate_budgetqualitygate_validateguardrail_check

Remote endpoint

Streamable HTTP

Hosted server - connect over the network, no local install.

url

https://plith.ai/api/mcp

Add to VS Code Add to Cursor

claude_desktop_config.json

json

{
  "mcpServers": {
    "plith": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://plith.ai/api/mcp"
      ]
    }
  }
}

Desktop config is stdio-only; this bridges via mcp-remote. Native remote: Settings > Connectors.

MCP Server: ai.plith/plith

🛠️ Key Features

🚀 Use Cases

⚡ Developer Benefits

⚠️ Limitations

dedupq_check

dedupq_complete

burnrate_estimate

burnrate_track

burnrate_optimize

burnrate_budget

qualitygate_validate

guardrail_check

guardrail_create_policy

pitfalldb_query

pitfalldb_report

rigor_plan

rigor_execute

rigor_status

rigor_workflows

Install

Remote endpoint

Related servers