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Stock Vectors

stock_vectors
Read-onlyIdempotent

Tuatara Embed Stocks: vector embeddings of listed stock symbols and of text in one space, built for the connections a sector label does not carry — which symbols an event, theme or headline moves, including companies it never names. Send sym:TICKER (e.g. sym:AAPL) for a symbol's own vector, or any text (a headline, a theme). With similarity_to_first=true the answer ranks inputs 2..n by cosine to input 1, so 'headline + candidate symbols in, ranked basket out' is one call. 4,096 dimensions, unit length. Up to 64 inputs per call; no account needed; nothing is stored.

Input Schema

NameRequiredDescriptionDefault
inputYeslist of texts to embed: sym:TICKER for a listed symbol's own vector, anything else is embedded as text; at most 64
encoding_formatNobase64 (default, float32 little-endian) or float (4,096 numbers)
similarity_to_firstNoalso return every other input's cosine to the first input, ranked (e.g. first = the headline, the rest = candidate symbols)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.3/5.0
Behavior5/5

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

Annotations already declare read-only, idempotent, non-destructive, closed-world behavior, so safety is covered. The description adds substantive context beyond that: 4,096 dimensions, unit length, a 64-input cap, no account required, and nothing stored. These are operationally useful traits an agent would not infer from the annotations alone.

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?

Front-loads the resource and purpose, then moves to invocation modes and behavioral facts. The sentences are dense and slightly long, but nearly every clause carries useful information. Minor trimming possible 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?

No output schema exists, so the description must explain returns; it does so with dimensionality, unit length, and the ranked similarity behavior. The encoding_format return shapes live in the schema. Complete enough for correct invocation, though output structure could be stated more explicitly.

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%, so all three parameters are already documented in the schema. The description reinforces the sym:TICKER convention and frames similarity_to_first as a ranked-basket operation, but adds little syntax or constraint detail beyond what the schema already states. Baseline 3 is appropriate.

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?

States a specific resource and operation: vector embeddings of listed stock symbols and arbitrary text in one shared space, with the distinct use case of capturing connections a sector label misses. An agent can tell exactly what this tool produces and why it differs from ordinary text embedding.

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

Usage Guidelines4/5

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

Gives clear invocation patterns: send sym:TICKER for a symbol's own vector, or any text, and use similarity_to_first=true to rank inputs 2..n against input 1. The 'headline + candidate symbols in, ranked basket out' example shows the intended workflow. No explicit when-not-to-use guidance, but there are no siblings to route between.

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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