Recent OHLCV bars for one symbol, most recent last.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | how many most-recent bars to return (default 120, max 1000) | |
| symbol | Yes | the NSE ticker, e.g. RELIANCE | |
| timeframe | No | 1d (default), 1w or 1mo |
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 present, so the description carries the full burden of behavioral disclosure. It does add one useful behavioral detail: 'most recent last' specifies the ordering of the returned bars. However, it does not mention whether the call is read-only, what happens for an invalid symbol, or any other edge-case behavior.
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, tightly written sentence that conveys the core resource, scope, and ordering behavior without any redundancy or fluff. It is front-loaded and every word earns its place.
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?
There is no output schema and no annotations, leaving the description as the only source of behavioral and return-value context. The one-line description does not explain the response structure, what fields are included, or how errors/pagination are handled. It is under-specified for an agent trying to use the tool correctly without additional information.
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 description coverage is 100%, with each parameter already having a clear description. The tool description adds no semantic detail about the parameters beyond what the schema provides, so the baseline score of 3 applies.
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 the resource ('OHLCV bars') and the scope ('for one symbol'), which clearly identifies the tool's function. It does not use a verb like 'retrieve' or 'return', but the tool name and the noun-phrase description make the purpose unambiguous. It is distinct from siblings like get_symbol and market_meta because it specifically targets OHLCV price data.
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 implies this tool is used when you need recent historical price bars for a single symbol, but it provides no explicit guidance on when to prefer it over related sibling tools such as get_symbol or market_meta. There is no when-not-to-use or alternative routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
