suggest
Autocomplete.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | O que já foi digitado. |
radar-cnpj
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Changed1 schema field changed
- added
Input schema / properties / q / descriptionAdded value: +"O que já foi digitado."
- First observed
TDQS
D1.7/5.0
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No information is given about side effects, read-only nature, or what the tool actually returns. The description does not disclose whether it is safe or if it modifies any state.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short, which makes it concise but also under-specified. While it is front-loaded with the only information, the brevity comes at the cost of clarity and usefulness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool lacks an output schema and any details about the return value or use case. With only the name and minimal parameter info, the description is incomplete and does not enable a caller to understand behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The parameter 'q' has a description in Portuguese ('O que já foi digitado') clarifying it is the user's typed input. However, it lacks format, examples, or constraints, providing only basic meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Autocomplete.' is vague, not specifying the resource, context, or type of suggestions. It names the feature but lacks a clear verb phrase or object, making the tool's exact purpose ambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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 like 'search' or 'list_watches'. There is no context about the appropriate scenario for calling 'suggest'.
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
C2.7/5.0
Disambiguation5/5
Each tool targets a clearly distinct purpose: exact CNPJ lookup, company search, autocomplete, AI filter generation, idea evaluation, reference data, session/watches management, and API utility endpoints. There is no meaningful overlap that would cause an agent to select the wrong tool.
Naming Consistency2/5
The naming is inconsistent: some tools use verb_noun snake_case (get_cnpj, add_watch, list_watches), some are bare English verbs (search, suggest, contact), and some are bare nouns or abbreviations (health, local, ref, api_index). It also mixes languages, with Portuguese avaliar alongside English names.
Tool Count4/5
13 tools is within a reasonable range for a server covering CNPJ lookup, search, evaluation, monitoring, and API metadata. The count feels slightly broad because several utility endpoints (health, api_index, contact, local) are auxiliary, but no tool is redundant.
Completeness3/5
The core lookup, search, evaluation, and reference workflows are covered well. However, the monitoring lifecycle is incomplete: add_watch and list_watches exist but there is no remove_watch, and monitor_session has no session status or termination counterpart, creating a dead end for the watch flow.