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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

TableJSON 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."
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.
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 / 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"
+}
  3. First observed

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already establish readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds meaningful behavioral context beyond annotations: the first-call/second-call sequencing and how the optional parameters relate to the first-call questions. It does not describe the output format, but the annotations lower the burden here.

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 two sentences, front-loads the most important usage instruction, and contains no filler. Every sentence contributes either call sequencing or return-value context.

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?

For a read-only guide tool, the description covers the key call pattern and high-level return value. However, with no output schema, the return format is left vague, and the 'no arguments' phrasing obscures the two required analytics parameters, so the description is not fully complete for correct invocation.

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?

Schema coverage is 100%, so the baseline is 3, and the description does add useful meaning by labeling role, market, and scale as optional second-call arguments. However, 'Call with no arguments on first contact' directly contradicts the schema's required context and llm_model, creating real risk that an agent invokes the tool without required parameters.

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 clearly states the tool's purpose: 'Returns what this session should do next,' naming a specific resource and the expected output. It does not explicitly distinguish itself from sibling tools, but its role as a session-orientation/guide tool is evident from the name and the sibling set.

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

Usage Guidelines3/5

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

The description gives explicit timing guidance: 'Call with no arguments on first contact' and use optional role/market/scale on a second call. However, it offers no alternatives or exclusions, and the 'no arguments' instruction conflicts with the schema's required context and llm_model parameters, making the guidance partially misleading.

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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