browse_wishes
Browse wishes from the Wishing Well. Discover what capabilities AI agents want.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| sort | No | newest | |
| limit | No | ||
| category | No | Filter by wish category |
agentstamp
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
C2.8/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden for behavioral disclosure. It doesn't state whether the operation is read-only, what the return format is, or any other behavioral details beyond 'browse'.
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?
Two short sentences, front-loaded with the main verb and resource. No empty filler; it's appropriately concise.
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?
For a tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the tool returns, how sorting/filtering works, or any behavioral context.
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 33%, and the description adds no parameter information. The schema provides some constraints (sort enum, limit bounds) but the description doesn't clarify meanings like 'most_granted' or valid categories.
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 clearly states the tool browses wishes from the Wishing Well and explains the purpose of discovering AI agent capabilities. It uses a specific verb and resource, but doesn't explicitly distinguish it from sibling tools like browse_agents.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or alternative tools for similar tasks.
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
B3.3/5.0
Disambiguation3/5
Several tools overlap in purpose, such as bridge_erc8004_lookup and bridge_erc8004_trust_check, trust_check, get_agent, and get_agent_reputation all returning trust or reputation data. Descriptions help clarify the distinctions, but the similarities could lead an agent to select the wrong tool.
Naming Consistency3/5
Naming uses a mix of conventions: verb-noun (get_agent, search_agents), noun-first (compliance_report, dns_discovery), and prefix-based groupings (trust_*, bridge_*). While snake_case is consistent, the inconsistent verb/noun ordering and synonyms (lookup, check, verify) make it less predictable.
Tool Count4/5
With 19 tools, the set is slightly heavy but still justified by the platform's broad feature set covering trust checks, reputation, teams, wishes, compliance, and credentials. Some tools could be merged (e.g., bridge_erc8004_lookup and bridge_erc8004_trust_check), but the count is not excessive for the apparent scope.
Completeness3/5
The tool surface focuses heavily on reading and checking trust data, but lacks obvious lifecycle operations such as updating or deleting agents, creating wishes, or managing endorsements. The presence of create_team without corresponding team management (update/delete) leaves a notable gap.