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Prepare secure bounty action

prepare_bounty_action
Idempotent

Use this when ChatGPT has gathered the required details conversationally and the person has explicitly confirmed a post, fund, solve or claim, complete, or verify action. Create one idempotent first-party authorization session; never request a wallet or verifier signature in ChatGPT and never describe prepared status as completion.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYes
detailsNoBounded action-specific draft or evidence fields for first-party review.
networkNo
bounty_idNo
actor_walletNo
opportunity_idNo
bounty_contractNo
idempotency_keyYes
amount_base_unitsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
paidYes
actionYes
statusYes
networkYes
bounty_idNo
intent_idYes
expires_atYes
next_actionYes
share_afterYes
actor_walletNo
opportunity_idNo
schema_versionYes
bounty_contractNo
confirmed_blockNo
transaction_hashNo
amount_base_unitsNo
authorization_urlYes
evidence_boundaryYes
canonical_event_idNo
canonical_event_kindNo
expected_canonical_eventsYes

Schema Changelog

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

  1. First observed

TDQS

A3.5/5.0
Behavior4/5

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

The description adds useful behavior beyond annotations: it establishes a first-party authorization session and forbids collecting wallet or verifier signatures in ChatGPT. It also clarifies that prepared status is not completion. There is no contradiction with idempotentHint=true, and the read/destructive hints are consistent with a non-read, non-destructive prepare operation.

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?

Two dense sentences with no padding, and the use-condition is front-loaded. The action-value list is a bit run-on and ambiguous ('solve or claim'), but the overall length is appropriate for the safety-critical content.

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

Completeness2/5

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

Given nine parameters, a nested details object, and a sibling cluster, the description is incomplete: it omits parameter semantics, does not distinguish prepare_bounty_action from prepare_bounty_post, and contains a schema mismatch. Output schema and annotations reduce some burden, but correct invocation still requires undocumented inference.

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

Parameters2/5

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

Schema description coverage is only 11%, so the description had to carry parameter meaning, but it does not explain idempotency_key, bounty_id, actor_wallet, network, amount_base_units, opportunity_id, or bounty_contract. The only parameter-related content is the list of action values, which also includes unsupported 'claim.' Required fields other than action are left to the agent to infer.

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?

States a clear conditional trigger (details gathered and explicit confirmation) and a specific outcome: create one idempotent first-party authorization session. It is not a tautology and clearly targets bounty actions. However, it does not distinguish itself from sibling prepare_bounty_post, and the action list includes 'claim' even though the schema enum omits it.

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?

Provides explicit when-to-use preconditions: details gathered conversationally and explicit confirmation of the action. It also adds strong guardrails, such as never requesting wallet or verifier signatures in ChatGPT and never describing prepared status as completion. It does not name sibling alternatives or state when to choose prepare_bounty_post or get_bounty_action_status, so exclusions and alternatives are missing.

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

A3.9/5.0
Disambiguation3/5

Most tools are distinct by domain (comments, feeds, on-ramp, competitions), but prepare_bounty_action and prepare_bounty_post both trigger on an approved posting flow, and list_autonomous_bounties vs get_bounty_feed vs inspect_open_competition_v2 present overlapping read surfaces. The long 'use this when' guards help, but an agent could still select the wrong prepare or list tool.

Naming Consistency4/5

The names are almost uniformly snake_case verb_noun with predictable prefixes like prepare_, get_, and list_. Minor inconsistencies: add_bounty_comment vs list_bounty_comments (singular/plural), the v2 suffix, and compile_objective_with_cloud_agent breaks the concise verb_noun pattern.

Tool Count4/5

Thirteen tools is within the normal range for a platform covering bounty lifecycle, comments, feeds, competitions, and fiat on-ramp. However, the generic prepare_bounty_action plus separate prepare_bounty_post and several read/list variants make the set feel slightly heavier than the core domain needs.

Completeness4/5

The core bounty lifecycle (post, fund, solve/claim, complete, verify), status checks, listing, comments, and sharing are all represented. Missing explicit update/cancel or single-bounty detail operations are minor gaps because the prepare/status model and feed data cover most agent workflows.

Resources