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One precomputed session-statistics result: P(outcome | conditions) for a preset on a hosted symbol, in the engine's full honesty envelope — the estimate with N and a Wilson 95% confidence interval, minimum-sample guards, a first-half vs second-half stability split, per-year counts, the value distribution where the outcome is continuous, and the disclaimer. Historical conditional frequencies, not predictions. Preset ids come from edge_presets; symbols from edge_symbols.

Input Schema

NameRequiredDescriptionDefault
presetYesPreset id, e.g. 'gap-fill' — see edge_presets
symbolYesHosted symbol, e.g. 'BTCUSDT' — see edge_symbols
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
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • 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.\""
  2. Added

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint and non-destructive behavior, so safety is covered; the description goes further by disclosing the returned envelope (N, Wilson 95% CI, minimum-sample guards, first/second-half stability split, per-year counts, distribution for continuous outcomes, disclaimer). The 'Historical conditional frequencies, not predictions' caveat is genuinely useful interpretive context, though it omits failure modes and any rate/availability limits.

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

Conciseness4/5

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

The core identity ('One precomputed session-statistics result') is front-loaded and the clause list maps to actual returned fields, so little is wasted. It is a single very dense sentence, though, and the trailing id-source sentence could be folded in without loss.

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 must carry return-value information, and it does so thoroughly, listing components of the result and a disclaiming caveat. What is missing is minor: no note on error conditions or sparse-data behavior beyond the mention of minimum-sample guards.

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% across all three parameters, including the detailed third-person 'context' wording, so the schema carries the semantics. The description only echoes the 'see edge_presets / edge_symbols' pointers already present in the schema, adding no format or syntax detail beyond it.

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 names a specific verb-plus-resource (a precomputed session-statistics report) and pins the exact quantity returned: P(outcome | conditions) for a preset on a hosted symbol. It also implicitly separates itself from edge_presets/edge_symbols by casting them as id sources rather than result producers, so an agent can route without opening schemas.

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?

It tells the agent where the required ids come from ('Preset ids come from edge_presets; symbols from edge_symbols'), which is real prerequisite guidance. However, it never states when to prefer this tool over data-fetching siblings such as trackers_query or propfirms_* tools, nor any when-not condition, so usage is only implied.

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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