list_watches
Lista watches da sessão.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| session_id | Yes |
Lista watches da sessão.
| Name | Required | Description | Default |
|---|---|---|---|
| session_id | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of behavioral disclosure. It establishes that this is a read/list operation, but it does not describe return format, behavior for invalid or missing sessions, ordering, pagination, or empty results. This is too thin for an agent to predict behavior confidently.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short, front-loaded, and free of filler. It captures the core operation efficiently, but it is so minimal that it omits useful behavioral context, which prevents a perfect score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, a one-phrase description is not complete. It does not explain what the tool returns, whether it can fail, or how errors are surfaced, leaving material gaps for an agent trying to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The phrase 'da sessão' adds some meaning by tying session_id to the session whose watches are being listed. However, schema description coverage is 0%, and the description does not clarify ID format, session state requirements, or ownership constraints, so it only partially compensates for the missing schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Lista' and names the resource 'watches', scoped to 'da sessão'. It clearly communicates that this tool lists watches for a given session, and the sibling tool add_watch provides a natural contrast even without an explicit distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given about when to use this tool versus alternatives like add_watch or monitor_session. The intended use case is only implied by the verb 'Lista', leaving the agent to infer when this is the right choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.