Skip to main content
Glama

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

onboard_start

Read-onlyIdempotent

Where am I in onboarding? Returns your registered agents, their structured capability manifests, a progress checklist, the Base Sepolia testnet config, and exactly what you can do now vs. still need.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
agentsYes
statusYes
testnetYes
owner_idYes
checklistYes
rest_authYes
can_do_nowYes
still_neededYes
integration_verifiedYes

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds meaningful context by enumerating precisely what the tool returns and framing it as a status query with an emphasis on actionable guidance ('what you can do now vs. still need'). No contradiction with annotations.

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?

One well-structured sentence front-loaded with the central question, followed by a compact list of return items. Each element in the list earns its place, though the enumeration is slightly long. Overall highly efficient and readable.

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?

For a zero-parameter, read-only status tool with an output schema and safety annotations, the description is complete: it states what it returns, why it matters ('what you can do now vs. still need'), and implicitly when to call it. There is no missing information an agent would need to invoke it 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 takes zero parameters, and the schema description explicitly states 'No arguments — the owner is derived from the authenticated principal.' With 100% schema coverage and no params to explain, the description has nothing extra to contribute; baseline 4 is appropriate.

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 opens with a clear question ('Where am I in onboarding?') and then names a specific verb ('Returns') with a concrete resource scope: registered agents, capability manifests, progress checklist, Base Sepolia testnet config, and actionable next steps. This distinguishes it from siblings like check_earnings and data_preview — it is unambiguously the onboarding-status tool.

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 implies use when the agent needs to determine onboarding progress or next steps ('exactly what you can do now vs. still need'), which provides clear context. However, it does not explicitly name alternatives or state when not to use it, leaving some routing to inference. Still, the question framing and payload list make the intended use obvious.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

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