Identity and latest session for one NSE symbol: name, ISIN, listing dates, liquidity tier, last close and average delivery percentage.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | the NSE ticker, e.g. RELIANCE |
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?
With no annotations, the description carries the behavioral burden, and it does convey the informational scope by listing return fields and 'latest session'. However, it doesn't explicitly state read-only behavior, data freshness limits, or behavior for unknown symbols, leaving some assumptions to the agent.
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 entire definition is a single dense sentence with no filler, and it front-loads the one-symbol scope before listing return fields. The wording is slightly clipped/grammatically awkward because it lacks a verb, so it doesn't reach a 5.
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 tool, the field list plus the schema's symbol documentation is enough to invoke it correctly. Still, without annotations or an output schema, it doesn't cover error behavior, output structure, or the exact meaning/market context of 'liquidity tier' and 'average delivery percentage', leaving some gap.
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 parameter is already well documented as an NSE ticker with the RELIANCE example. The description's 'one NSE symbol' reinforces the scope but adds no new semantic detail, so the baseline 3 is appropriate.
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 identifies the resource (one NSE symbol) and enumerates the returned data fields (ISIN, listing dates, liquidity tier, last close, delivery percentage), making the tool's purpose clear. It lacks a proper verb—'Identity and latest session' is a noun phrase—and does not explicitly differentiate from search_symbols, so it falls just short of a 5.
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?
There is no guidance on when to choose get_symbol over alternatives like search_symbols or get_bars. An agent must infer from the word 'one' and the field list that this is for exact-symbol lookups, but no when-to-use or when-not-to-use guidance is given.
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.
