search_symbols
Find NSE symbols by ticker or company name.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | maximum results (default 10) | |
| query | Yes | ticker or company name, whole or partial |
Find NSE symbols by ticker or company name.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | maximum results (default 10) | |
| query | Yes | ticker or company name, whole or partial |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only says 'Find' and describes the search input, without mentioning partial matching behavior, result limits, ordering, case sensitivity, or output format. The read-only nature is implied but not explicitly detailed.
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?
The description is a single, concise, front-loaded sentence with no filler. It is efficient, though it could afford to include more behavioral or usage detail without becoming verbose.
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 simple two-parameter search tool with full schema coverage, the basic call is documented. However, there is no output schema and the description does not explain the return format or how results are ordered, which leaves the agent with partial information about what to expect.
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?
The input schema already documents both parameters with 100% coverage, so the baseline is 3. The description repeats 'ticker or company name' but adds no new meaning beyond the schema and does not clarify limit semantics or defaults.
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?
The description states a specific verb ('Find'), a resource ('NSE symbols'), and the search criteria ('by ticker or company name'). It is clear what the tool does, though it does not explicitly differentiate it from the closely related sibling get_symbol.
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?
The description gives no guidance on when to use search_symbols versus alternatives such as get_symbol. There are no exclusions, prerequisites, or conditions provided, leaving the agent to infer usage from the tool name and context.
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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Each tool targets a clearly distinct action: symbol discovery, symbol metadata, price bars, query reference, scan execution, saved scan management, market metadata, and historical replay. run_scan and hitrate are differentiated as current matches versus past performance, so there is no meaningful overlap.
Most tool names follow a lowercase snake_case verb_noun pattern such as delete_scan, get_bars, run_scan, save_scan, and search_symbols. A few names like hitrate, market_meta, and sift_reference break that pattern but remain consistent in style and readable.
Ten tools is well-scoped for an NSE stock screener. Each tool earns its place and together they cover symbol lookup, market data, querying, saved scan lifecycle, and historical evaluation without redundancy.
The core screener workflow is covered: find symbols, fetch bars and metadata, learn the query language, run scans, manage saved scans, and evaluate hit rates. Minor gaps exist, such as no dedicated view for a single saved scan's full query and get_bars only returning recent bars, but these are workable limitations.