all_exchangesReturn metadata for all crypto exchanges tracked by CoinDesk Data (formerly CryptoCompare): name, country, grade, trading pairs, fee, and website URL.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {}
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Complete exchange list",
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare read-only, open-world, and idempotent behavior; the description aligns by stating it returns metadata. No contradictions, and the description adds minor clarity about the data source.
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 one concise sentence that efficiently lists the returned fields (name, country, grade, trading pairs, fee, website URL) without unnecessary details.
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 listing tool with no parameters, the description provides sufficient context by specifying the source and the fields returned, making it complete.
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?
There are no parameters, so schema coverage is complete. The description does not need to explain parameters.
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 returns metadata for all crypto exchanges tracked by CoinDesk Data, which distinguishes it from coin-related tools and other listing endpoints.
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 does not provide explicit guidance on when to use this tool versus alternatives, though the name is self-explanatory. It lacks any mention of conditions or comparisons.
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.
Several tool families overlap heavily: ask_pipeworx, its beta, and grounded variants, the six polymarket_* tools, and the three price endpoints (price, price_full, price_multi) can be confused despite distinct purposes. Long descriptions provide some disambiguation, but an agent must read carefully to select the correct tool.
Tool names mostly follow snake_case with verb_noun or noun_noun patterns (all_coins, compare_entities, top_market_cap), and related families share clear prefixes (histo_*, polymarket_*). Minor deviations exist (bare verbs like remember/forget, brand names like ask_pipeworx), but the overall pattern is readable and consistent.
With 46 tools, the server is far beyond the 3-15 well-scoped range and nearly double the 25-tool threshold for 'too many'. Many tools are unrelated to the server's apparent crypto purpose (generate_llms_txt, scan_dependency, memory helpers), making it feel like a general-purpose utility rather than a focused data service.
The crypto data surface is fairly complete (spot, historical, top lists, news, social stats, exchange metadata), and the Pipeworx meta-tools (ask_pipeworx, deep_research, entity_profile) cover a broad range of factual queries. However, there are notable gaps: no direct way to fetch pipeworx:// citation URIs, and no advanced crypto order-book/trade endpoints, leaving some workflows as dead ends.