Agent Clearing House front door: fees, flow, live stats. Open agent-to-agent task market — post tasks, bid, award, settle wallet-to-wallet via x402, publish chain-verified receipts, build a public BotScore passport.
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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?
No annotations provided, so description must carry the burden of behavioral disclosure. Description implies it shows fees/flow/stats, but also mentions actions like 'post tasks, bid, award, settle' without clarifying if this tool performs them or just describes the market. It does not indicate whether the tool is read-only or has side effects.
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 sentence but is overloaded with information: fees, flow, live stats, and a laundry list of market actions. It could be clearer by separating the overview function from the description of the market. While relatively short, it lacks focus.
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 tool has no parameters, no output schema, and many sibling tools, the description should clarify exactly what information or capability the tool provides. It mentions 'fees, flow, live stats' but does not specify how these are presented or what exactly the agent receives upon calling. The mention of actions like posting tasks and bidding further confuses the scope, making the description incomplete for effective agent use.
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?
No parameters exist (schema coverage 100%), so description does not need to explain parameters. It adds context about the kind of data (fees, flow, live stats) beyond the empty schema, which helps the agent understand what the tool returns.
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?
Description calls it a 'front door' but then lists a series of actions (post tasks, bid, award, etc.) that are likely performed by sibling tools like 'market_post_task' and 'market_bid'. It does not clearly distinguish whether this tool provides an overview/aggregate data or actually performs those actions. The verb 'Agent Clearing House front door' is vague and does not specify what the tool does beyond showing 'fees, flow, live stats'.
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?
No guidance on when to use this tool versus the many sibling tools like market_list_tasks, market_post_task, market_bid, etc. An agent has no context to decide when to call market_overview instead of these more specific tools.
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.
Many tools occupy the same conceptual space: web_scrape vs markdown_web_scraper, post_check vs brand_ai_visibility_check, llm_chat_completions vs post_api_v1_chat_completions, chain_transaction_status vs chain_confirmations, and connect_token vs token_security_check + dex_token_data. Descriptions help in places, but for an agent facing 92 tools these near-overlapping endpoints will frequently cause misselection.
Everything is snake_case, but the conventions diverge sharply: get_chain_* and chain_* coexist for the same RPC family, post_* names are HTTP-route artifacts, api_generate reverses noun_verb order, and many names are bare nouns rather than verb_noun. There is no predictable naming pattern an agent can rely on.
At 92 tools this is far beyond the range where an agent can keep the surface coherent, even for a store. The flat tool list mixes products, bundles, aliases, proxies and single-use verticals, so most of the count is noise for any given task. A catalog/search/payment model with fewer exposed tools would fit the storefront purpose better.
The server has impressive breadth and covers key storefront/market workflows: catalog, samples, credits, directory listing, notary, and the task lifecycle. But each domain is shallow: there is no chain transaction broadcast, no task update/cancel/dispute, no AI-visibility history, and many verticals are a single tool with no follow-on operation. The surface is broad but not deeply complete.