Skip to main content
Glama

search_sessions

Search and filter AI agent sessions to debug runs, compare performance, and analyze LLM usage patterns from observability providers.

Instructions

[Deprecated: Use aiobs_search_sessions] Search and filter AI agent sessions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNo
labelsNo
providerNo
modelNo
functionNo
afterNo
beforeNo
has_errorsNo
evals_failedNo
limitNo
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'search and filter,' implying a read-only operation, but doesn't describe traits like pagination, rate limits, authentication needs, or what happens with no results. The deprecation note adds some context, but overall, behavioral details are insufficient for a tool with 10 parameters.

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?

The description is concise and front-loaded with the deprecation warning, followed by the core purpose. It uses two short sentences with no wasted words, making it easy to parse. However, it could be more structured by separating deprecation from usage instructions.

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 the complexity (10 parameters, no schema descriptions, no annotations, no output schema), the description is incomplete. It doesn't explain return values, error handling, or how parameters affect results. The deprecation note adds some context, but overall, it's inadequate for guiding an agent in selecting and invoking this tool effectively.

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 0%, so the description must compensate by explaining parameters. It only mentions 'search and filter' generically, without detailing what 'query,' 'labels,' 'provider,' etc., mean or how they interact. With 10 undocumented parameters, this adds minimal value beyond the schema, failing to clarify semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool's purpose as 'Search and filter AI agent sessions,' which is clear but vague. It doesn't specify what resources or data are searched (e.g., session metadata, logs) or how filtering works, and it doesn't distinguish from siblings like 'list_sessions' or 'aiobs_search_sessions' beyond the deprecation note. This is a basic statement of function without specificity.

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?

The description provides minimal guidance: it explicitly states '[Deprecated: Use aiobs_search_sessions]' to indicate an alternative, but offers no context on when to use this tool versus other siblings like 'list_sessions' or 'langfuse_search_sessions.' It lacks information on prerequisites, use cases, or exclusions, leaving the agent with little direction.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/neuralis-in/shepherd-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server