Get SEC Failures-to-Deliver (FTD) data for a stock. High FTD quantities may indicate naked short selling or settlement issues.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Number of days of history (default 90) | |
| ticker | Yes | Stock ticker symbol (e.g. 'GME', 'TSLA') |
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; it adds interpretive context (e.g., high FTD may indicate naked short selling) but does not disclose behavioral traits like return format, pagination, or error handling. The schema covers parameters, but the description lacks operational details.
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 extremely concise with two sentences: the first states the core purpose, and the second adds relevant interpretation. No unnecessary words; every sentence 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?
The tool has only two parameters and no output schema; the description is adequate for a simple data retrieval tool but lacks information about the output structure (e.g., time series, field explanations). For completeness, it could describe what the returned data contains.
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%, so the schema already documents both parameters. The description adds value by explaining the significance of FTD data (naked short selling, settlement issues), which is not in the schema. This contextual richness justifies a slight above-baseline score.
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 clearly states the tool retrieves SEC Failures-to-Deliver (FTD) data for a stock, using the specific verb 'Get' and naming the resource. It distinguishes itself from sibling tools like get_stock_price or get_insider_trades by focusing on a niche data type.
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 FTD data is needed, but it does not explicitly mention when not to use it or provide alternatives. The context from sibling tools suggests a clear domain, but explicit exclusion or comparison is missing.
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 distinct resource and action, and descriptions explicitly disambiguate similar pairs (e.g., get_congress_member vs get_congress_trades, get_crypto_holder vs get_crypto_holders). No two tools appear to do the same thing.
The dominant pattern is get_<noun>, with list_<noun> for enumerations. Minor deviations exist: a bare 'search' tool and the 'sec_' prefix on SEC filing tools break the uniform verb_noun style, but the pattern remains predictable.
At 24 tools, this is on the heavier side, but the server spans multiple financial data domains (SEC filings, stocks, insider trading, crypto, rates, economics), so each tool has a clear purpose. It is just below the 'too many' threshold.
The tool surface is remarkably complete for financial data retrieval: SEC filing lifecycle is covered (list -> index -> document), institutional ownership is available from both stock and institution perspectives, and insider/congress/crypto/economic data are all present. No obvious gaps or dead ends.