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Glama

Tiingo

by io.github.pipeworx-io

Stock Prices

stock_prices
Read-onlyIdempotent

Get EOD historical stock prices for a ticker (open, high, low, close, volume, adjClose). Without a start_date, returns just the latest trading day. Example: stock_prices({ ticker: "AAPL", start_date: "2024-01-01", end_date: "2024-01-31", frequency: "daily" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYesStock ticker symbol, e.g. "AAPL", "MSFT", "TSLA". Share classes accept either separator — "BRK.B" and "BRK-B" both work.
_apiKeyNoOptional — your own Tiingo API key for higher limits; omit to use the shared Pipeworx key.
end_dateNoEnd date in YYYY-MM-DD format (optional)
frequencyNoResample frequency (default "daily")
start_dateNoStart date in YYYY-MM-DD format (optional). Omit to get only the latest day.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "ticker": "AAPL"
-  },
-  {
-    "end_date": "2024-12-31",
-    "frequency": "daily",
-    "start_date": "2024-01-01",
-    "ticker": "TSLA"
-  }
-]New value: +[
+  {
+    "ticker": "AAPL"
+  },
+  {
+    "ticker": "BRK.B"
+  },
+  {
+    "end_date": "2024-12-31",
+    "frequency": "daily",
+    "start_date": "2024-01-01",
+    "ticker": "TSLA"
+  }
+]
    • changedInput schema / properties / ticker / description
      Previous value: -"Stock ticker symbol, e.g. \"AAPL\", \"MSFT\", \"TSLA\""New value: +"Stock ticker symbol, e.g. \"AAPL\", \"MSFT\", \"TSLA\". Share classes accept either separator — \"BRK.B\" and \"BRK-B\" both work."
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "ticker": "AAPL"
+  },
+  {
+    "end_date": "2024-12-31",
+    "frequency": "daily",
+    "start_date": "2024-01-01",
+    "ticker": "TSLA"
+  }
+]
  3. First observed

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover readOnly, idempotent, and non-destructive behavior, so the description does not need to repeat those. It adds valuable behavior beyond annotations: omitting start_date returns only the latest trading day, and it lists the returned price fields. This is meaningful context for a read-only tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two concise sentences plus one useful example. It front-loads the core purpose, immediately adds the key behavioral nuance about start_date, and uses the example to demonstrate parameter usage without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only tool with no output schema, the description compensates by naming the return fields and explaining the latest-day behavior. Minor gaps exist around end_date defaults and frequency resampling semantics, but the schema covers frequency and end_date format, so the description is largely complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description goes beyond schema by explaining the start_date omission behavior and providing a complete, realistic example that maps all main parameters together. That extra semantic clarity justifies a 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: 'Get EOD historical stock prices for a ticker' and enumerates the exact output fields (open, high, low, close, volume, adjClose). This clearly separates it from sibling tools like crypto_prices and stock_metadata by domain and purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear context: the tool is for end-of-day stock price history, and even clarifies the default behavior when start_date is omitted. It does not explicitly mention alternatives, but the 'stock' wording and sibling list make selection obvious without misleading guidance.

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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TDQS

A3.7/5.0
Disambiguation2/5

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.

Naming Consistency3/5

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.

Tool Count2/5

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.

Completeness2/5

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.

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