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Glama

Tiingo

by io.github.pipeworx-io

Crypto Prices

crypto_prices
Read-onlyIdempotent

Get crypto prices for a pair (open, high, low, close, volume) at a chosen resample frequency. Example: crypto_prices({ ticker: "btcusd", resampleFreq: "1day" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYesCrypto pair ticker, e.g. "btcusd", "ethusd"
_apiKeyNoOptional — your own Tiingo API key for higher limits; omit to use the shared Pipeworx key.
resampleFreqNoResample frequency, e.g. "1min", "1hour", "1day" (default "1day")

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "ticker": "btcusd"
+  },
+  {
+    "resampleFreq": "1hour",
+    "ticker": "ethusd"
+  }
+]
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare read-only and idempotent behavior, and the description adds that the tool returns OHLCV data and supports resampling frequencies. It does not cover rate limits or API key behavior beyond the schema, but with strong annotations the bar is lower and the added output context is valuable.

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 sentences with an illustrative example, front-loaded with the core action and resource. Every word contributes to understanding, with no fluff or redundancy.

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

Completeness5/5

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

The description includes the key output fields and a usage example, which is sufficient for a low-complexity read-only tool with rich annotations and fully described schema. No output schema exists, but the description covers the essential return information.

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

Parameters3/5

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's example helps illustrate parameter usage but does not add technical semantics beyond what the schema already provides. It is consistent with the schema and adds marginal convenience.

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?

The description clearly states the tool's purpose with a specific verb ('Get'), a specific resource ('crypto prices'), and the exact data fields (open, high, low, close, volume). It distinguishes itself from sibling tools like stock_prices by focusing on cryptocurrencies and resample frequency.

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?

The description gives clear context that this tool is for retrieving crypto price data, with an example showing typical usage. It does not explicitly mention alternatives or exclusions, but the crypto focus and example make the intended use case unambiguous.

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.

Resources