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Run project feasibility analysis

runFeasibilityAnalysis

Enqueue a fresh deterministic analysis run for a saved project. Pass projectId, or project with the name the user said; with neither, the result lists their projects and nothing is queued. Returns immediately with agentStatus (queued, running, current, failed, or none) and a receipt id; does not block on completion. Irreversible: a queued run cannot be un-queued from MCP. Re-check with getProjectContext, but no more than once every 15 seconds — it is a multi-query bundle, not a status endpoint.

Input Schema

NameRequiredDescriptionDefault
contextYesExplain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
projectNoProject name to match, when you do not have the UUID. Case-insensitive; an exact name wins over a partial one. Matching more than one lists the candidates instead of guessing.
projectIdNoProject UUID. Omit if you pass `project`.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • removedInput schema / properties / llm_model
      Removed value: -{
-  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
-  "type": "string"
-}
    • changedInput schema / required
      Previous value: -[
-  "context",
-  "llm_model"
-]New value: +[
+  "context"
+]
  2. Changed1 schema field changed
    • removedInput schema / properties / conversation_id
      Removed value: -{
-  "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.",
-  "type": "string"
-}
  3. Changed4 schema fields changed
    • changedInput schema / properties / context / description
      Previous value: -"Explain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): \"Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization.\""New value: +"Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
    • addedInput schema / properties / conversation_id
      Added value: +{
+  "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.",
+  "type": "string"
+}
    • addedInput schema / properties / llm_model
      Added value: +{
+  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "context"
-]New value: +[
+  "context",
+  "llm_model"
+]
  4. Changed3 schema fields changed
    • addedInput schema / properties / project
      Added value: +{
+  "description": "Project name to match, when you do not have the UUID. Case-insensitive; an exact name wins over a partial one. Matching more than one lists the candidates instead of guessing.",
+  "maxLength": 200,
+  "minLength": 1,
+  "type": "string"
+}
    • changedInput schema / properties / projectId / description
      Previous value: -"Project UUID to analyse."New value: +"Project UUID. Omit if you pass `project`."
    • changedInput schema / required
      Previous value: -[
-  "projectId",
-  "context"
-]New value: +[
+  "context"
+]
  5. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Adds substantial context beyond annotations: non-blocking return, the full agentStatus enum, a receipt id, and the key irreversibility warning that a queued run cannot be cancelled from MCP. That irreversibility caveat is exactly the kind of thing destructiveHint=false and idempotentHint=false cannot convey on their own, and it is consistent with them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loads the action in the first sentence, then layers the conditional parameter behavior, return contract, and polling guard in descending order of importance. No sentence is filler and nothing is repeated.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description does the needed work by naming the return fields and their possible values. It leaves unspecified what the analysis itself yields and how a typical run duration should be communicated to a user, which matters for a non-blocking operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already carries the per-parameter detail, but the description adds the cross-parameter semantics that matter: projectId vs project name precedence and the neither-supplied branch. The `context` analytics parameter is left unmentioned in prose, which is fine given the schema documents it fully.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Enqueue a fresh deterministic analysis run for a saved project') and implicitly separates itself from siblings like getFeasibilitySnapshot and explainFeasibilityStatus, which read rather than enqueue. The agent can tell what it produces (a queued run) without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly routes the follow-up check to getProjectContext and warns against using it as a poll endpoint more than once per 15 seconds, and describes the no-identifier fallback (lists projects, nothing queued). It stops short of saying when not to enqueue at all, e.g. when agentStatus is already 'current' and a snapshot read would suffice.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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