stock_metadataGet metadata for a stock ticker: company name, description, exchange code, and the date range of available EOD price data. Example: stock_metadata({ ticker: "AAPL" })
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes | Stock ticker symbol, e.g. "AAPL". Share classes accept either separator — "BRK.B" and "BRK-B" both work. | |
| _apiKey | No | Optional — your own Tiingo API key for higher limits; omit to use the shared Pipeworx key. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / ticker / descriptionPrevious value: -"Stock ticker symbol, e.g. \"AAPL\""New value: +"Stock ticker symbol, e.g. \"AAPL\". Share classes accept either separator — \"BRK.B\" and \"BRK-B\" both work."Input schema / examplesAdded value: +[
+ {
+ "ticker": "AAPL"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the read-only, idempotent, non-destructive nature. The description adds value by specifying exactly what metadata is returned, including the EOD date range, which is not visible in annotations or an output schema. No rate limits or auth details are disclosed, but these are less critical for a simple read-only lookup.
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?
One clear sentence with a concrete inline example and zero filler. The output fields are front-loaded, making the tool's purpose immediately understandable without extra reading.
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 simple read-only metadata lookup, the description plus schema and annotations cover everything an agent needs: what the tool does, what parameters to pass, and what data will be returned. No critical information is missing.
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 both ticker and _apiKey fully documented in the schema. The description only reuses the ticker parameter in an example and adds no new semantic meaning beyond the schema. Baseline 3 is therefore appropriate when the schema carries the parameter documentation weight.
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 a clear verb ('Get') and resource ('metadata for a stock ticker'), and enumerates the specific outputs (company name, description, exchange code, date range of available EOD price data). This clearly differentiates it from sibling tools like stock_prices, which focus on price history.
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 provides a concrete usage example but does not explicitly state when to use this tool over alternatives or when not to use it. Sibling differentiation is only implicit through the word 'metadata' versus price-focused siblings, so the agent must infer the usage boundary.
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 have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded differ only in mode; several Polymarket tools (polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, bet_research) target related opportunities; ai_visibility_check and scan_competitor_ai_presence overlap. The meta-tools (discover_tools, suggest_questions, pipeworx_trending) could also be confused for one another.
All names are lowercase snake_case, which is consistent, but patterns vary: some are verb_noun (ask_pipeworx, resolve_entity, validate_claim), others are noun (news, crypto_prices, stock_metadata), and several use brand prefixes (pipeworx_*, polymarket_*). This mixed convention is readable but not predictable.
35 tools is too many for a coherent, well-scoped server. The set bundles a financial data API (Tiingo) with a generic data router (ask_pipeworx), prediction-market tools, memory utilities, subscription management, and npm checks — many unrelated to the server's apparent purpose, making it feel bloated.
For a Tiingo server, core data coverage is limited to stock prices, stock metadata, crypto prices, and news — missing real-time quotes, fundamentals, forex, technical indicators, and other typical Tiingo endpoints. Conversely, the general Pipeworx platform has broad query/research/subscription coverage but that domain doesn't align with the server name, leaving significant functional gaps.