Kaleidoscope MCP (SEC & financial data)

Find the right tool

discover_tools

Read-onlyIdempotent

Call this FIRST when you're not sure which tool fits a question. Returns the top tools matching your query (semantic search over tool descriptions), each with its full input schema — so you can call the right tool in one round trip without a follow-up list_tools. The catalog has many tools spanning SEC-filing signals, fundamentals, 13F institutional ownership, 13D/13G activist positions, news/press-release search, and (incoming) biotech pipeline data. Use this any time the question is fuzzy or could plausibly match multiple tools.

Example queries:

'who are the largest institutional holders of a ticker'
'find news about FDA approvals in 2026'
'how is smart money positioned in semiconductors'
'manager track record vs the S&P'
'pipeline of a biotech as of a past quarter'

Returns top-k matches sorted by similarity, each with name, title, description, and inputSchema. Pick the best match and call it directly.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "discover_tools"
  }
}

Input Schema

Name	Required	Description	Default
`k`	No	How many top matches to return (default: 10, max: 25).
`query`	Yes	Natural-language description of what you want to do.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 10, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already cover safety (readOnly, idempotent, non-destructive, closed-world), so the description's job is to add mechanism — and it does: semantic similarity ranking, each match returned with its full inputSchema, and names the returned fields (name, title, description, inputSchema). It stops short of stating what happens on a weak or zero-match query, which is the remaining behavioral gap.

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 critical instruction is front-loaded in the first sentence, and the return-format note lands last where it belongs. Slightly long: the catalog inventory sentence and the parenthetical '(incoming)' add bulk that could be trimmed without loss.

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?

There is no output schema, and the description fully compensates by describing the return shape and ordering. For a two-parameter, read-only discovery tool, an agent has everything needed to decide to call it and to use the result.

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?

Schema coverage is 100%, so baseline is 3, but the example queries add real meaning about what a good 'query' string looks like (natural-language questions, not keywords), and 'top-k matches sorted by similarity' clarifies how k behaves. This is useful semantics beyond the terse schema descriptions.

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 verb and resource ('discover_tools' / semantic search over tool descriptions) and explicitly positions itself against a named alternative ('without a follow-up list_tools'). The catalog scope sentence tells an agent exactly which domains it can match against, so the tool's function is unambiguous before opening the schema.

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?

Gives an explicit triggering rule ('Call this FIRST when you're not sure which tool fits a question'), an additional inclusive condition ('any time the question is fuzzy or could plausibly match multiple tools'), and an implied exclusion via the list_tools contrast. The five example queries make the trigger concrete rather than abstract.

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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