Skip to main content
Glama
tinhien11

tdai-memory-mcp

by tinhien11

capture

Save decisions, learnings, and task outcomes to memory for AI agents. Use it after completing non-trivial tasks, making decisions, or fixing bugs to retain important context.

Instructions

Save a decision, a learning, or a task outcome to memory. Call this tool after you complete a non-trivial task, make a decision, or fix a bug with a known root cause. You can capture a single text string, or a list of role-based conversation messages.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoOptional tags.
typeYesThe type of the memory.
contentNoThe text to remember. The tool redacts secrets before it stores the text. Use this for a single message. Use 'messages' instead for a multi-turn conversation.
task_idNoThe task ID. Use this to isolate memory by a specific task. Link captures to a task for finer isolation.
team_idNoThe team ID. Use this to isolate memory by team. When set, all queries filter by this value.
user_idNoThe user ID. Use this to isolate memory by user within a team. When set with team_id, queries filter by both.
agent_idNoThe agent ID. Use this to isolate memory by agent role within a team. Defaults to the detected agent.
messagesNoA list of role-based conversation messages to capture. When set, 'content' is ignored. The tool flattens the messages into a single text for search, and stores the original messages for retrieval.
metadataNoOptional metadata.
verifiedNoSet this to true to mark the capture as verified. Verified captures rank higher in recall.
supersedesNoThe ID of a capture that this one replaces. The old capture is marked as stale and ranks lower.
session_keyNoThe session key. The default is hash(cwd).
override_rejectionNoSet this to true to force capture even if the content was previously rejected. Use this only when the rejection reason no longer applies.
Behavior3/5

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It states the tool 'Save[s]... to memory' and mentions both single-text and message-list capture modes, but it doesn't describe side effects (e.g., superseding marks old captures stale) or return values. The minimal disclosure is viable but not rich, relying on the schema for deeper details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with purpose, and each sentence earns its place. It states what the tool does, when to use it, and the input modes without redundant words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has 13 parameters and no output schema, but the comprehensive schema covers parameter meaning. The description supplies the crucial 'when to call' context and high-level input options. It doesn't state return values, but for a memory-capture tool this gap is acceptable given the schema's richness and the clear distinction from sibling tools.

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 description coverage is 100%, so the schema already documents all 13 parameters. The description adds conceptual grouping ('decision, learning, task outcome' align with the type enum and the content vs messages distinction) but doesn't introduce syntax or format details beyond the schema. This matches the baseline of 3 for high schema coverage.

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

Purpose5/5

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

The description opens with 'Save a decision, a learning, or a task outcome to memory,' which is a specific verb (Save) plus resource (memory). It clearly distinguishes the tool from sibling read/delete tools like recall, search, and forget, and enumerates concrete use cases for when to capture, such as after a non-trivial task or bug fix.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

'Call this tool after you complete a non-trivial task, make a decision, or fix a bug with a known root cause' provides explicit when-to-use triggers. It doesn't list alternatives or exclusions, but the context of sibling tools (recall/search/forget) makes the usage unambiguous, so a 4 is appropriate.

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/tinhien11/tdai-memory-mcp'

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