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oliverwehn

trade-analytics-mcp

by oliverwehn

resolve_symbol

Resolve ambiguous symbol mentions into precise Databento notation and trading metadata: root, point value, tick size, and front-month data.

Instructions

Resolve a symbol the user mentioned (e.g. 'MNQ', 'micro nasdaq', 'MNQU6') to its precise Databento notation + trading metadata: root, continuous symbol, dataset, point value, tick size, and best-effort front-month. Runs locally under your Databento key. Free.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNo
queryYes
Behavior4/5

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

With no annotations, the description carries full burden. It states the tool runs locally, is free, and requires a Databento key, which provides essential behavioral context. It does not mention side effects (likely none), rate limits, or caching, but the disclosure is adequate for a read-only resolution tool.

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 three sentences, front-loading the core purpose with examples in the first sentence. The subsequent two short sentences add behavioral context without waste. Very efficient and easy to parse.

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?

Given the tool's simplicity (2 params, no output schema), the description covers inputs implicitly, outputs (lists fields), and behavioral context (local, free, key). A minor gap is the lack of explicit date parameter documentation, but overall it is reasonably complete.

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 0%, so the description must explain both parameters. It clearly explains 'query' with examples ('MNQ', 'micro nasdaq'), but the 'date' parameter is only indirectly referenced via 'front-month' and lacks explicit explanation of its purpose or format. This leaves a gap.

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 uses the specific verb 'Resolve' and resource 'symbol', providing concrete examples ('MNQ', 'micro nasdaq') and listing the output fields (root, continuous symbol, etc.). It clearly differentiates from sibling tools like get_price_context, render, and analyze, which have different purposes.

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 implies usage when a user mentions a symbol needing resolution, but it does not explicitly state when not to use the tool or how it compares to alternatives like get_price_context. Sibling tools are not addressed, leaving room for ambiguity.

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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