Skip to main content
Glama

ingest_context

Ingests raw text, discussion, or logs into a temporal knowledge graph for analysis using LangGraph orchestration.

Instructions

Ingest raw context into the temporal knowledge graph using LangGraph background orchestration. Args: context: The raw text, discussion, or logs to analyze.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contextYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations provided, so description must disclose behavior. Only mentions 'background orchestration' implying async processing, but fails to state write/destructive nature, auth requirements, rate limits, or side effects. Insufficient for safe tool use.

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?

Description is brief (two sentences plus arg doc). No redundant information. Appropriate length for a single-parameter tool.

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 no annotations, an output schema exists but return value is not described. With many sibling tools, the description misses opportunity to clarify uniqueness. Agent may confuse with memory_context_pack or remember_context.

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?

Schema coverage is 0%, so description must compensate. It adds 'raw text, discussion, or logs' as context for the 'context' parameter, providing meaningful clarification beyond type='string'. However, no further detail on format or limits.

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?

Description clearly states verb 'Ingest', resource 'raw context into temporal knowledge graph', and method 'using LangGraph background orchestration'. It is specific and distinguishes from generic tools, though not explicitly from siblings like remember_context.

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 on when to use this tool vs alternatives (e.g., remember_context, memory_context_pack). No context on prerequisites or anticipated outcomes.

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/Snehgabani/elite-reasoning-mcp'

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