Run a credit check before committing money. Predicts the success probability of an escrow before you create it — analyzes both agents' completion rates, AgentRank graph trust, prior transaction history, and Sybil risk. Your agent skips bad deals before any funds are locked.
| Name | Required | Description | Default |
|---|---|---|---|
| buyerDid | Yes | Decentralized Identifier (e.g. did:key:z...) | |
| sellerDid | Yes | Decentralized Identifier (e.g. did:key:z...) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It explains the analysis inputs (completion rates, AgentRank, transaction history, Sybil risk) and frames the tool as a pre-commitment analysis, which implies read-only behavior. However, it doesn't explicitly state whether the tool has no side effects, requires specific permissions, or how the result is delivered, leaving some ambiguity about its operational behavior.
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 three sentences, each serving a distinct purpose: it opens with a clear usage directive, explains what the tool does and the inputs it analyzes, and closes with a benefit statement. There is no redundancy, and the front-loaded phrase 'Run a credit check before committing money' immediately communicates the core intent.
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 (2 required params, no output schema, no annotations), and the description covers purpose, timing, and analysis factors. The phrase 'predicts the success probability' sufficiently conveys the expected return type. There are no critical gaps that would prevent an agent from selecting and invoking the tool correctly.
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 for parameters is 100%, with each DID parameter already well-described with pattern and example. The description adds context by referring to 'both agents' and the analysis factors, linking the parameters to their role in the prediction. However, this is complementary rather than essential, so the baseline of 3 holds.
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 identifies the tool's purpose with a specific verb ('predicts') and resource ('success probability of an escrow'). It explicitly differentiates from the sibling tool 'create_escrow' by stating 'before you create it' and 'before any funds are locked', making the tool's unique role unmistakable.
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 provides strong context on when to use the tool: 'before committing money' and 'before you create it'. It clearly implies a pre-check step, though it doesn't explicitly mention alternatives or exclusions. The timing guidance is specific enough to guide the agent to use this prior to create_escrow.
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.
Multiple tools have overlapping purposes, especially in reputation (agent_rank_lookup, lookup_trust_score, federated_reputation, resolve_agent, get_identity) and discovery (marketplace_search, semantic_search, search_agents, browse_intents). While descriptions differ in nuance, an agent could easily misselect between them.
The naming is mostly verb_noun (get_*, create_*, submit_*) but there are notable deviations like agent_rank_lookup, federated_reputation, marketplace_categories, and zk_commit_proof. Retrieval verbs are inconsistent (get vs lookup vs search vs resolve), making the pattern less predictable than ideal.
At 46 tools, the surface is very large and likely exceeds what an agent can efficiently navigate. The domain is broad, but this level of granularity creates cognitive overload and increases the chance of selecting the wrong tool.
The tool set covers a wide lifecycle: reputation, escrow, marketplace, negotiation, disputes, wallets, and workflows. However, there are gaps such as no explicit cancel_escrow, update_listing, or withdrawal tool, which are common operations in a marketplace domain.