list_saved_scans
List the scans saved to the authenticated Sift account.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
List the scans saved to the authenticated Sift account.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 provided, the description carries the full burden, and it does establish the core behavioral context: 'List' signals a safe read operation, 'saved' scopes it to persisted scans, and 'authenticated Sift account' conveys the auth requirement. However, it discloses nothing about pagination, ordering, limits, or response shape, which a list operation typically involves.
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?
A single 10-word sentence with the verb front-loaded and zero filler. Every word earns its place: 'List' states the action, 'saved' scopes the resource, and 'authenticated Sift account' sets the access context.
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 zero-parameter, zero-complexity list operation, the description is sufficient for correct invocation: no inputs are needed and the scope plus auth requirement are stated. Since no output schema exists, the return format (a list of saved scans) is implied but not detailed, which is a minor gap given how simple this tool is.
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 is empty with additionalProperties: false and zero parameters, so there are no parameter semantics to document. Per the zero-parameter baseline of 4, the description has nothing it needs to add, and nothing is ambiguous.
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 uses a specific verb ('List') and a precisely qualified resource ('scans saved to the authenticated Sift account'), making the operation unmistakable. It is clearly distinct from sibling tools like run_scan, save_scan, and delete_scan by verb and the 'saved' qualifier, but it stops short of explicitly naming these alternatives.
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?
No when-to-use guidance, exclusions, or alternative routing is provided. Given siblings like run_scan, save_scan, delete_scan, and hitrate, an agent must infer when listing saved scans is the right choice rather than executing, saving, or deleting one. The context is implied at best.
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.