quoteFetch the current real-time quote for a ticker symbol from FMP, including price, change, percent change, day range, 52-week range, volume, and market cap.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive behavior, so the description is not burdened with safety disclosure. The description adds useful context by naming FMP as the data source and enumerating the returned fields, but it does not disclose latency or rate-limit behavior, which would have added extra value.
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, front-loaded sentence that lists all key output fields without unnecessary verbiage. Every phrase adds value, making it highly concise and well-structured.
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?
Given the tool's simplicity (one parameter, no output schema), the description covers the essential return elements (price, change, ranges, volume, market cap) and names the data provider. It could have mentioned that the result is a single quote object or noted that the symbol must be valid, but overall it is sufficiently complete for its complexity level.
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?
With 0% schema description coverage, the description must compensate for the single 'symbol' parameter. It does clarify that the parameter is a 'ticker symbol', which provides essential meaning, and the schema example 'AAPL' reinforces this. However, it does not elaborate on symbol format or potential validation rules, leaving some ambiguity.
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 ('Fetch') and clearly identifies the resource ('current real-time quote for a ticker symbol'), listing key data fields. It distinguishes itself from siblings like quote_short by emphasizing the full real-time quote detail, making the purpose unmistakable.
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 clearly indicates when to use this tool (when a current real-time quote is needed) and implies it is not for historical data. However, it does not explicitly mention alternatives like historical_price or quote_short, so it falls short of full comparative guidance.
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.
Multiple tools overlap in purpose: quote/quote_short/historical_price/intraday for price data; balance_sheet/income_statement/cash_flow for financials; search_symbol/search_name/discover_tools for lookup; and a cluster of Pipeworx routers (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim) with unclear boundaries. Agents will frequently select the wrong tool.
All tool names use consistent snake_case, but naming conventions vary widely: noun phrases (balance_sheet, entity_profile), bare verbs (forget, subscribe), verb+noun (compare_entities, resolve_entity), and adjective+noun (historical_price, recent_alerts). No single pattern dominates, making it harder to guess tool names.
55 tools is excessive for a server labeled 'Fmp'. The core financial data tools are perhaps 20-25, while the rest are unrelated: memory utilities, prediction market analyzers, web scraping, and meta-routing tools. This bloated set dilutes the server's purpose and burdens the agent with irrelevant options.
For the declared domain (FMP financials), the set covers the main statements but lacks tools like segment data, insider trades (listed as paid), or ownership details (also paid). Conversely, it includes many tools for prediction markets and general data retrieval that don't belong here, creating a mismatch between server name and actual capability.