Pay $0.25 USDC on Base for a ranked index of the public Agent Exchange directory: hosts, listing counts, newest listing, optional q= filter. Does not write listings. GET /directory is the free raw list. POST /directory/list ($1) is how you get listed.
| Name | Required | Description | Default |
|---|---|---|---|
| x_payment | No | Signed x402 payment payload |
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?
Since no annotations are provided, the description carries the full disclosure burden. It reveals the cost ($0.25 USDC), the read-only nature (does not write listings), and the ranked index output. It also mentions the optional q= filter, but does not specify return format or error handling. Still, it is quite transparent for a simple API.
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 entire description is one tight sentence that packs purpose, cost, behavior, and alternatives. No redundant words. The key info is front-loaded and every part earns its place.
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?
The tool is simple (one optional parameter, no output schema). The description covers its purpose, cost, read-only behavior, and distinguishes sibling endpoints. It could mention pagination or exact response shape, but such details are not required for this scale. It is sufficiently complete for the complexity level.
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?
Schema coverage is 100% (the single parameter x_payment has a description). The description adds context about the payment amount and the read-only behavior, but does not elaborate on the x_payment signature format or how to construct it. It also mentions an optional q= filter not present in the schema, which could cause confusion. Overall, it adds marginal value beyond the schema, aligning with the baseline for high coverage.
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's purpose: a paid, ranked index of the Agent Exchange directory, including specific elements (hosts, listing counts, newest listing, optional q= filter). It also distinguishes itself from sibling GET /directory (free raw list) and POST /directory/list (getting listed), so it is unambiguous and specific.
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?
Explicitly states when to use this tool versus alternatives: it contrasts with the free raw list (GET /directory) and the listing submission endpoint (POST /directory/list). It also clarifies that it does not write listings, so the user knows this is read-only and paid. This gives clear decision guidance.
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.