senti-mcp-server

Read the MQL5 authoring rules

get_authoring_conventions

Read-only

Retrieve MQL5 authoring constraints before generating source to avoid static-analysis rejections and costly serial compile failures.

Instructions

Read the Senti Quant MQL5 authoring contract as data: the hard-safety constraints, the trading-safety requirements, the static analyzer's forbidden-construct list, and the platform limits on draft count and source size. CALL THIS BEFORE GENERATING ANY MQL5 SOURCE. Code that breaks these rules is rejected by a static scan before it reaches the compiler, and compile slots are globally serial, so discovering a rule by failing a compile is expensive and still fails. The response is small (~2 KB) and static per deploy. forbiddenConstructs[].pattern values are regular expressions reported verbatim — this tool does not evaluate them.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_authoring_conventions"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`limits`	Yes
`forbiddenConstructs`	Yes
`hardSafetyConstraints`	Yes
`tradingSafetyRequirements`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Addedv2.8.0Aug 22, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Beyond the readOnlyHint annotation, the description adds behavioral specifics: the response is small (~2 KB) and static per deploy, and forbiddenConstructs[].pattern values are regular expressions reported verbatim without evaluation. These details give an agent accurate expectations about call cost, response stability, and the tool's pass-through behavior, all of which are not visible in annotations or the empty input schema.

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?

Every sentence earns its place. The first sentence defines the resource, the second gives a direct action, the third provides the cost rationale, and the final two disclose response size and regex handling. The content is front-loaded with the most important information and uses imperative language for the critical instruction.

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?

The description is complete for a read-only, zero-parameter tool. An output schema exists, so return-value detail is not the description's responsibility, but it still tells the agent the response is small, static, and that regexes are verbatim. The sibling-tool context shows no overlap, so no alternative-routing information is needed. An agent knows exactly when to call it and what to expect.

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?

The input schema has zero parameters, so the baseline is 4. The description does not need to explain parameter meanings; instead it clarifies what the returned data represents, which is the closest equivalent. It names the four content categories and the pattern semantics, adding meaning beyond the empty schema.

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 opens with a specific verb and resource: 'Read the Senti Quant MQL5 authoring contract as data'. It enumerates four concrete components of that contract and the platform limits on draft count and source size. This clearly differentiates it from all sibling tools, which deal with drafts, accounts, brokers, strategies, positions, and performance—not authoring rules.

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 gives an explicit, imperative trigger: 'CALL THIS BEFORE GENERATING ANY MQL5 SOURCE.' It also explains the cost of ignoring that guidance: rule-breaking code is rejected by a static scan before the compiler, and compile slots are globally serial, making compile-time discovery expensive. This is unambiguous when-to-use guidance with a clear rationale.

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

Deploy Server