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Glama

Kawal

by io.github.PugarHuda

Search the roster by problem

find_agents
Read-onlyIdempotent

Search the BSC roster by describing the problem rather than naming a product, and get back agents ranked with Kawal's evidence attached. Duplicate registrations are collapsed: roughly two thirds of the newest registrations are copies of a template, and returning all of them would be returning the same agent many times. With trending, the list is instead what 8004scan's visitors looked at most over that window — attention, not evidence — each entry carrying the tier Kawal's own record gives it, which is where the two disagree.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many to return, at most 20.
queryNoWhat you need done, in plain words. Optional when `trending` is given; then it filters the trending list by name or description.
chainIdNoChain id. One of 56, 97. Defaults to 56.
trendingNoReturn 8004scan's trending agents for this window instead of a search, in the registry's order.

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description adds meaningful behavioral context: duplicate registrations are collapsed, trending returns attention-based results rather than evidence-based ones, and each entry carries Kawal's tier. This materially helps an agent understand what the tool will actually return.

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 front-loaded with the core purpose in the first sentence, and every subsequent sentence adds relevant behavioral detail about duplicates and trending. It is dense but not bloated, with no wasted words.

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?

Given the 100% parameter schema coverage, rich annotations, and no output schema, the description provides enough context for an agent to call this tool correctly: it explains search behavior, trending behavior, duplicate handling, and result ranking. No critical selection or invocation information is missing.

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?

Schema coverage is 100%, so the schema already fully documents each parameter. The description adds semantic value by explaining how `query` behaves when `trending` is given, how `trending` changes the result semantics from evidence to attention, and why duplicate collapsing matters.

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 searches the BSC roster by problem description and returns ranked agents with Kawal's evidence attached, which distinguishes it from product-name lookups. It does not explicitly name alternative sibling tools, but the purpose is specific and well-scoped.

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 gives clear context on when to use the search mode versus the trending mode, and explains that query is optional when trending is used. It does not explicitly enumerate when-not-to-use cases or alternatives among sibling tools, but the guidance is sufficient for selecting the right mode.

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

All Kawal toolsInspect
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TDQS

A4.2/5.0
Disambiguation4/5

Most tools have distinct angles—live single check, history, paid deep report, comparison, owner sweep—so misselection is unlikely in practice. However, verify_agent, uptime_history, and deep_report all probe one agent and could be confused without reading descriptions carefully.

Naming Consistency4/5

Six of nine tools follow the verb_noun pattern (check_payment, verify_agent, find_agents), and all names are readable snake_case. The exceptions—agents_by_owner, deep_report, and uptime_history—are noticeable but not chaotic.

Tool Count5/5

Nine tools is well-scoped for a monitoring and verification service. Each tool covers a distinct user need with no obvious redundancy.

Completeness4/5

The set covers search, live verification, uptime history, reputation source analysis, payment checks, comparison, owner listing, and mandate planning. Minor gaps like pagination on agents_by_owner and find_agents prevent a higher score.

Resources