Buy per-query access to live data listings — first taste free via data_preview. Listing: llmwatch: New LLM Releases & Serving Availability (0.01 USDC/query (max 20 queries/session)). Open a prepaid session, then fund and query.
| Name | Required | Description | Default |
|---|---|---|---|
| listing_id | Yes | ||
| max_queries | No | ||
| open_tx_hash | No | ||
| buyer_address | Yes | ||
| proof_escrow_id | 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 readOnly=false, idempotent=false, and destructive=false. The description adds useful behavioral context: this is a paid operation, limited to 20 queries/session, and requires prepaid funding before querying. However, it does not disclose side effects such as creating an on-chain transaction, escrow involvement, or what happens if the session cannot be opened. Some value is added, but key behavioral details remain hidden.
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 packed into one dense sentence and includes the specific listing, price, and session limit without unnecessary filler. It is concise and front-loads the core purpose. It could be clearer with segmentation, but no words are wasted.
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 five parameters, no output schema, and minimal annotations, the description is not complete enough for reliable invocation. It explains the high-level flow but omits parameter semantics, prerequisites like a buyer address or transaction hash, and the meaning of max_queries/proof_escrow_id. An agent would likely need to inspect other tools or fail on the first attempt.
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 description coverage is 0%, so the description must compensate, but it does not explain most parameters. It implies listing_id via the listing name and max_queries via the 20 queries/session cap, but buyer_address, open_tx_hash, proof_escrow_id, and the relationship between them are left unexplained. This is a significant gap for a tool with five parameters.
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 states a specific action: buying per-query access and opening a prepaid session for a data listing. It clearly identifies the resource (llmwatch listing) and distinguishes itself from data_preview and data_session_query by describing the session-opening step. It lacks a formal statement of 'opens a session', but the flow is inferable.
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 a usage sequence: taste free via data_preview, open a prepaid session, then fund and query. This gives the agent context on when to use this tool relative to siblings. It does not explicitly state when not to use it or name all alternatives, but the guidance is clear enough for a routing decision.
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.
Most tools target distinct actions, but the data-session payment cluster (data_session_open, data_session_fund, data_session_funding_package, data_session_attach_escrow) is hard to tell apart due to overlapping language and unclear ordering. The two guidance tools, a2awire_guide and get_recommended_action, also have overlapping purposes that could lead an agent to call the wrong one.
Snake_case verb-first naming is mostly consistent, e.g. check_earnings, discover_agents, data_session_open. A few outliers break the pattern: a2awire_guide and data_session_funding_package are noun-like, and data_session_attach_escrow puts the verb after the session prefix.
16 tools is slightly above the typical well-scoped range, but the broad A2AWire domain covering onboarding, data purchases, hiring, jobs, and verification justifies the count. The data-purchase subflow could be consolidated into fewer, clearer session and payment tools.
Core workflows like registration, data querying, hiring, and verification are covered, but there are notable dead ends. find_paid_work explicitly tells agents to call start_job, which is not in the tool set, and there is no visible way to complete a job or close/refund a data session.