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Kanishka-dabas

Financial Data MCP Server

Search Ticker

search_ticker

Resolve a company name to matching stock ticker symbols. Returns up to five ranked candidates with symbol, name, exchange, and security type so you can identify the correct ticker.

Instructions

Resolve a company name to its stock ticker symbol(s).

Use this first when you have a company name (e.g. "Apple", "Tata Motors") rather than an exact ticker. Returns up to 5 candidate matches with symbol, name, exchange, and security type, ranked by relevance.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
company_nameYesFull or partial company name, e.g. "Apple" or "Microsoft Corporation".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden and does well: it states the tool returns up to 5 ranked candidate matches and lists the returned fields. It does not explicitly mention no-match/error behavior, but the read-only search semantics are clear from the description.

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?

Three concise sentences, each earning its place: purpose, when-to-use, and return behavior. The most important information is front-loaded, and there is no redundant filler.

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?

For a simple one-parameter resolver, the description covers what input to provide, what the tool does, and what comes back. The presence of an output schema covers formal return structure. A small gap is the absence of explicit failure or no-match behavior, but that is minor for tool selection.

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?

The schema has 100% description coverage for its single parameter, so the baseline is 3. The description adds examples like 'Apple' and 'Tata Motors' and reinforces partial-name support, but it does not substantially go beyond the schema's own 'Full or partial company name' explanation.

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 a specific verb ('Resolve') and clear resource: a company name to stock ticker symbol(s). It distinguishes itself from sibling tools by establishing that it maps names to tickers, whereas siblings like get_stock_quote_tool operate on already-known tickers.

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?

'Use this first when you have a company name rather than an exact ticker' gives explicit guidance on when to invoke the tool. It implies the inverse condition (do not use it when you already have an exact ticker), though it does not name specific sibling alternatives.

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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