search_instrumentsFind NSE stock symbols from company names, nicknames, or spoken phrases to resolve user input before quoting or ordering.
Find NSE stocks by company name, spoken name or symbol (for example 'reliance', 'state bank', 'L and T', 'infy'). Use this to turn what the user said into a symbol before quoting or ordering.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Company name, nickname or symbol, as the user said it. |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false; the description usefully reinforces the closed NSE universe and the tolerance for spoken/nickname forms. It does not disclose what comes back (single best match vs. ranked list) or what happens on ambiguous or zero-result queries, which is the main behavioral unknown for a search tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no filler. The capability statement comes first, the usage directive second, and the examples are embedded inline rather than in a separate list.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter lookup with no output schema, the description covers input well but says nothing about the result shape or ambiguity handling. An agent knows how to call it but not whether to expect one instrument or a candidate list, which affects whether it must confirm with the user.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3, but the examples add real meaning beyond the schema: they show that phonetically-spelled or transliterated forms ('L and T', 'infy', 'state bank') are legitimate queries. That is exactly the kind of guidance an agent needs when converting speech to a query string.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource ("Find NSE stocks") and enumerates the three matching keys: company name, spoken name, symbol. Concrete query examples ('reliance', 'L and T', 'infy') make it unmistakable against siblings like get_quote or get_orders.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
"Use this to turn what the user said into a symbol before quoting or ordering" gives a clear triggering condition and implicitly points at get_quote/place_order as downstream tools. It stops short of naming those siblings or saying when not to use it (e.g. when a symbol is already known), so it falls just short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.