market_snapshotCrypto market snapshot in one call: prices + fear/greed + trending + BTC fees. ~$0.02.
| Name | Required | Description | Default |
|---|---|---|---|
| ids | No | Coin ids to include |
| Name | Required | Description | Default |
|---|---|---|---|
| result | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only indicate readOnlyHint=false, destructiveHint=false, and openWorldHint=true, leaving the description to carry some burden. It adds useful cost information ('~$0.02') and the aggregation of multiple data types, but does not disclose potential side effects or behavior when ids are omitted. No contradiction with annotations, but could offer more behavioral detail.
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 immediately states the tool's purpose and contents, followed by cost information. Every word adds value, making it highly concise and appropriately 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 simple one-parameter schema with 100% coverage and the existence of an output schema, the description sufficiently covers the snapshot's content and cost. It lacks only explicit usage guidance relative to sibling tools, but overall it is complete for a tool of this complexity.
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?
The input schema covers 100% of the parameter (ids) with a description, so the baseline is 3. The tool description does not add any additional semantic context for the parameter beyond what the schema already provides, but it does imply the parameter controls which coins are included by listing prices as part of the snapshot.
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 provides a crypto market snapshot with specific components (prices, fear/greed, trending, BTC fees), using a clear imperative style. It distinguishes itself from the sibling crypto_prices by offering a broader multi-part snapshot in one call.
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 phrase 'in one call' implies this is for efficiently gathering a comprehensive market overview, but it does not explicitly state when to use this tool over alternatives like crypto_prices or web_search. The intended use case is inferred from the listed contents rather than explicitly guided.
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.
Four image generation tools, three video tools, and five 'ask' tools create significant overlap. Although descriptions specify the model, an agent must carefully compare prices and capabilities to choose correctly, making misselection likely.
All tool names use snake_case, but patterns are mixed: some start with verbs (remove_bg, scrape_page), some with nouns (crypto_prices, market_snapshot), and many use ai_/ask_ prefixes. Model suffixes like flux, gpt, pro, kling are descriptive but not systematically applied.
24 tools is heavy, inflated by near-duplicate variants for image, video, and LLM queries. While the broad scope justifies a large count, the redundant tools could have been consolidated.
The toolset covers a wide range of media and data tasks: image, video, music, voice, vision, LLM, web, crypto, domain, and endpoint discovery. Notable gaps like speech-to-text or image editing exist, but the surface is fairly complete for a general-purpose media toolkit.