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Read-onlyIdempotent

Call with no arguments on first contact. Optional role, market, and scale answer the first-call questions on a second call. Returns what this session should do next.

Input Schema

NameRequiredDescriptionDefault
roleNoWho is asking. Answers "Developer, GC, or municipal reviewer?".
scaleNoOne site or several. Answers "Single parcel, or a portfolio?".
marketNoWhich country the site is in. Answers "US or Canada?".
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."

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 / market
      Added value: +{
+  "description": "Which country the site is in. Answers \"US or Canada?\".",
+  "enum": [
+    "US",
+    "CA"
+  ],
+  "type": "string"
+}
    • addedInput schema / properties / role
      Added value: +{
+  "description": "Who is asking. Answers \"Developer, GC, or municipal reviewer?\".",
+  "enum": [
+    "developer",
+    "gc",
+    "reviewer"
+  ],
+  "type": "string"
+}
    • addedInput schema / properties / scale
      Added value: +{
+  "description": "One site or several. Answers \"Single parcel, or a portfolio?\".",
+  "enum": [
+    "single_parcel",
+    "portfolio"
+  ],
+  "type": "string"
+}
  5. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds real behavioral value by disclosing the two-call pattern and that the output is directional next-step guidance rather than data. It does not say what happens if optional params are supplied on the first call or whether they persist.

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?

Three short sentences, zero filler, and the most important instruction (call with no arguments first) is front-loaded before the optional refinement detail.

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

Completeness3/5

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

With no output schema, the description carries the burden of explaining the return value, and 'what this session should do next' stays abstract — an agent cannot anticipate whether it gets prose instructions, tool recommendations, or a checklist. Combined with the unresolved required-`context` conflict, the definition is usable but leaves gaps for a first-contact tool.

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

Parameters2/5

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

The description explains the sequencing of the three optional enums (they answer the first-call questions on a second call), which the schema alone does not convey. However, it instructs 'Call with no arguments on first contact' while the schema marks `context` as required — a direct conflict that will confuse an agent about whether it can call with zero arguments.

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

Purpose4/5

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

The description states concretely that the tool returns 'what this session should do next' and frames the call pattern (first contact vs. second call), which makes the onboarding/guidance purpose clear in combination with the name and title. It stops short of saying outright that it returns the MCP usage guide itself, so the resource is implied rather than named.

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?

It gives an explicit call sequence: no arguments on first contact, then optionally pass role/market/scale on a second call to answer the first-call questions. There is no realistic alternative sibling for onboarding, so the absence of named alternatives costs nothing.

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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