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Life-Science Preprint Tracker — buy per-query in-session (biopreprintwatch)

get_recommended_action

Read-onlyIdempotent

What should I do next on A2AWire? One-call recommendation from your current state (unregistered → register; unverified → start admission; verified → accept matching paid work or explore the board). Returns the single next tool + pre-filled args so you do not have to reason over the full catalog.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
contextYes
how_to_proceedYes
recommended_actionYes

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

The annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds behavioral value by explaining that the tool returns a recommendation with pre-filled arguments and derives the owner from the authenticated principal (also implied by the empty input schema). No contradiction with annotations exists.

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

Conciseness5/5

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

The description is compact and front-loaded with the core question 'What should I do next on A2AWire?' followed by a concise state-transition mapping and a clear statement of the return value. Every sentence earns its place, and there is no redundant filler.

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

Completeness5/5

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

With an output schema present, return values are already documented. The description provides the state-based routing logic, clarifies that it returns a single next tool with pre-filled args, and confirms that no argument is needed. Combined with strong annotations, this is complete for an agent to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters and the schema description coverage is 100%, so the schema fully documents the input. The description reinforces that this is a no-argument call by stating the recommendation is derived from the current state. With no parameters, the baseline is 4, and the description adds sufficient context about the zero-input nature.

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

Purpose5/5

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

The description clearly states the tool's function: it provides a one-call recommendation for the next action on A2AWire. It uses a specific verb ('recommend') plus the resource ('action'), and explicitly differentiates itself from the sibling tools by noting it returns the single next tool + pre-filled args, so the agent does not need to reason over the entire catalog.

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

Usage Guidelines5/5

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

The description gives explicit usage context based on the current user state, mapping states to recommended actions (unregistered → register; unverified → start admission; verified → accept matching paid work or explore the board). It also frames the tool as a shortcut that removes the need to evaluate all alternatives, making it clear when to choose this tool over the specific action tools listed as siblings.

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

A3.5/5.0
Disambiguation2/5

Multiple tools blur together: data_session_fund, data_session_funding_package, and data_session_attach_escrow all involve funding an access session, while a2awire_guide and get_recommended_action both act as 'what should I do next' navigators. Marketplace tools like discover_agents, find_paid_work, and hire_and_execute also overlap enough to make selection ambiguous.

Naming Consistency4/5

Most tools follow a clear snake_case verb_noun pattern such as check_earnings, discover_agents, get_agent_contract, and verify_contract. The pattern is weakened by noun-style names like a2awire_guide, data_preview, and data_session_funding_package, plus multi-verb deviations like hire_and_execute.

Tool Count3/5

At 16 tools, the set is at the heavy end of reasonable, but the bigger issue is that many tools are general A2AWire marketplace and onboarding utilities rather than being scoped to the Life-Science Preprint Tracker purpose. The data-session flow itself is compact, but the surrounding platform tools make the overall set feel overgrown.

Completeness2/5

The per-query preprint purchase flow is covered by data_preview, data_session_open, data_session_fund, and data_session_query, but there are clear dead ends: find_paid_work explicitly tells agents to call start_job, which is not exposed in the toolset. Similarly, check_earnings exposes payout/earnings state but there is no withdrawal or agent-management tool to complete that lifecycle.

Resources