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Glama

New LLM Release Tracker — buy per-query in-session (llmwatch)

by com.a2awire

data_preview

Read-onlyIdempotent

✅ No API key needed — call this now. Listing: llmwatch: New LLM Releases & Serving Availability. Price 0.01 USDC/query (max 20 queries/session). Sample questions: Which LLMs were released in the last 24 hours?; What is the context window and price of the newest Anthropic model?. FREE preview — no key, no payment. Try one of the sample questions now.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugNoPublic listing slug. Defaults to the routed session's listing when connected via /mcp/data/{slug}/http.
questionNoOptional free-text question you'd ask this data (echoed back).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4/5.0
Behavior5/5

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

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses key behavioral constraints: no authentication needed, no payment required, a 20-query session limit, and the 0.01 USDC/query pricing context. The schema's 'public metadata only' also clarifies the scope of data exposed. There is no contradiction with the annotations.

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

Conciseness3/5

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

The description is short and front-loaded, but it is promotional and somewhat redundant: 'No API key needed' is repeated as 'FREE preview — no key, no payment.' The emojis and exclamatory tone reduce precision, and the pricing line could be misread as applying to the preview itself.

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

Completeness4/5

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

The tool is simple, has no required parameters, and both parameters are well documented in the schema. The description adds the important auth and session-limit behavior. A return-format description is missing, but the low complexity and the 'public metadata only' scope make the definition reasonably complete.

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 description coverage is 100%, and both slug and question have meaningful descriptions including default routing behavior and echo semantics. The tool description adds sample questions but no additional parameter-level detail, so the baseline of 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?

The description communicates that this is a free preview of a data listing with a concrete resource (llmwatch) and sample questions, and the input schema clarifies it as a 'Free, guest-callable taste of a listing (public metadata only).' It does not explicitly name or distinguish itself from paid/session-based siblings like data_session_query, so it stops short of full sibling differentiation.

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

Usage Guidelines4/5

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

The description clearly tells the agent that no API key or payment is required, offers concrete sample questions, and directs immediate use ('call this now', 'Try one of the sample questions now'). It gives a clear context for when to use the tool, but it does not state when not to use it or explicitly point to alternatives.

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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TDQS

A3.7/5.0
Disambiguation3/5

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.

Naming Consistency4/5

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.

Tool Count4/5

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.

Completeness3/5

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.

Resources