get_contextRetrieve relevant context from local memory for a task or question, using optional tags and type filters to get precise, useful information.
Return compact local memory context for a task or question.
| Name | Required | Description | Default |
|---|---|---|---|
| kind | No | ||
| tags | No | ||
| limit | No | ||
| query | No | Task or question to retrieve context for. | |
| namespace | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.7Input schema / properties / kindAdded value: +{
+ "enum": [
+ "memory",
+ "conclusion"
+ ],
+ "type": "string"
+}Input schema / properties / namespaceAdded value: +{
+ "minLength": 1,
+ "type": "string"
+}Input schema / properties / tagsAdded value: +{
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+}v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It only says 'compact local memory context' without specifying if it is read-only, what side effects exist, or what 'compact' entails (e.g., summarization). This is insufficient for informed selection.
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 a single sentence, which is concise but too terse for the tool's complexity. It omits essential details about parameters and use cases, making it under-specified.
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?
Given 5 parameters, no annotations, no output schema, and many sibling tools, the description is grossly incomplete. It does not explain what the tool returns, how to use parameters, or how it relates to siblings like 'search_memory' or 'recall_memory'.
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 description adds no information about any of the 5 parameters. Schema coverage is only 20% (only 'query' has a description). The agent must infer the meaning of 'kind', 'tags', 'limit', and 'namespace' from the schema alone, which is insufficient.
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 clearly states the verb ('Return') and resource ('compact local memory context') for a task or question. It differentiates from siblings like 'search_memory' by specifying 'compact' context, but does not fully clarify how it differs from 'recall_memory' or 'ask_memory'.
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 usage guidance is provided. The description does not indicate when to use this tool vs alternatives like 'search_memory' or 'recall_memory', nor any conditions or limitations.
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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curl -X GET 'https://glama.ai/api/mcp/v1/servers/hacksurvivor/pathmark'
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