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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

TableJSON 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.
llm_modelYesThe 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.
projectIdNoProject UUID. Omit if you pass `project`.
conversation_idNoEcho 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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. 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"
+]
  2. 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"
+]
  3. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations provide readOnlyHint=false, idempotentHint=false, and destructiveHint=false. The description adds the critical behavioral trait that it returns immediately and does not block on completion, which is beyond the annotations. It also states that a queued run is irreversible, which is important behavioral context. However, it doesn't fully explain the failure modes or what happens if the project is not found, but it does cover the key behavior. The description also adds the deterministic nature, which is useful. Slight deduction for not detailing error handling, but overall strong.

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?

The description is compact yet dense, with critical information front-loaded: it starts with the action, then the identification options, then the return behavior, and ends with the polling limitation. Every sentence adds value, and it is appropriately sized for a tool with multiple important caveats.

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

Completeness5/5

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

Given the complexity of the tool (5 parameters, async behavior, irreversible action, polling guidance), the description covers everything an agent needs to know to call it correctly. It explains the return value, the non-blocking nature, the irreversibility, the polling constraint, and the identification logic. No output schema exists, but the description explains the return fields. This is comprehensive.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all five parameters. The description adds guidance on using project vs projectId and the fallback behavior, which is beyond the schema. It also explains the context and llm_model parameters are for analytics only. This adds value, but the schema already covers the basics, so a 3 is appropriate.

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

Purpose5/5

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

The description states a specific verb ('enqueue'), resource ('fresh deterministic analysis run'), and target ('saved project'). It distinguishes from siblings by noting it is a queueing operation that returns immediately, unlike status endpoints like getProjectContext or uiGetAnalysisStatus. It also clarifies the deterministic nature and the project identification alternatives, making it clearly different from other project-related tools.

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

Usage Guidelines5/5

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

The description explicitly explains when to use this tool: when you have a projectId or a project name, and states the fallback behavior if neither is given. It also gives guidance on re-checking with getProjectContext but limits to once every 15 seconds, noting it is a multi-query bundle. This provides clear context for when to use versus alternatives.

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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