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AgentSLA

by io.agentsla
OverviewDiscussionsAdmin

discover_monitored_apis

Search and filter 21,095 SLA-monitored M2M APIs and MCP connectors across 21 categories on Base Mainnet.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "discover_monitored_apis"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMax results to return (default 50, max 200)
searchNoOptional keyword search across service_name and endpoint_url (e.g. solana, stripe, github, brave)
categoryNoOptional category (e.g. mcp-connectors, ai-models, data-retrieval, autonomous-agents, web3-rpc, financial, security, cloud, developer_tools)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • changedInput schema / properties / category / description
      Previous value: -"Optional category filter: ai-models, web3-rpc, defi-data, data-retrieval"New value: +"Optional category (e.g. mcp-connectors, ai-models, data-retrieval, autonomous-agents, web3-rpc, financial, security, cloud, developer_tools)"
    • addedInput schema / properties / limit
      Added value: +{
+  "description": "Max results to return (default 50, max 200)",
+  "type": "number"
+}
    • addedInput schema / properties / search
      Added value: +{
+  "description": "Optional keyword search across service_name and endpoint_url (e.g. solana, stripe, github, brave)",
+  "type": "string"
+}
  2. First observed

TDQS

B3.4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It usefully discloses corpus size, category count, network (Base Mainnet), and that results are SLA-monitored, which tells the agent the data is curated. It does not state that the operation is read-only, how pagination/limits behave beyond the schema, or what the response looks like, leaving meaningful gaps for an unannotated tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with zero filler; the scale and scope figures are the most decision-relevant facts and come first. It is efficient but so terse that it could have spent one more clause on usage without becoming bloated.

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

Completeness3/5

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

For a simple three-parameter, all-optional search tool with no output schema, the description covers what is searched and over what corpus. It stops short of describing result ordering, whether empty filters return everything, or what fields each result carries, which an agent would need to use it confidently.

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 coverage is 100%, so the schema already documents limit, search, and category with examples. The description adds only the '21 categories' context that frames the category parameter, with no extra syntax, matching rules, or combination semantics beyond what the schema states. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb pair (search and filter) against a clearly named resource (SLA-monitored M2M APIs and MCP connectors), and quantifies scope with 21,095 entries, 21 categories, and Base Mainnet. It does not explicitly distinguish itself from verify_endpoint_sla, but the discovery-vs-verification split is inferable from the wording.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied by the word 'Search' — an agent can reasonably infer this is the entry point for finding APIs. There is no explicit when-to-use/when-not guidance, no mention of whether this should precede verify_endpoint_sla, and no note on whether search or category should be combined.

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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