agent-memory
Server Details
Persistent semantic memory for AI agents: store and recall text by meaning (RAG, Vectorize). x402
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- agishub/agishub-mcp
- GitHub Stars
- 0
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Tool Definition Quality
Average 3.6/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: one for storing text and one for semantically searching. There is no overlap or ambiguity.
Both tools follow a consistent verb_noun pattern (memory_search, memory_upsert), making the naming predictable and clear.
With only two tools, the set feels thin for a general memory server. While it covers basic store and retrieve, it may be insufficient for more complex use cases, but it is not extreme.
The tool set lacks essential operations like deleting, listing, or updating memories. This creates significant gaps that could lead to agent failures when memory management is required.
Available Tools
2 toolsmemory_searchAInspect
Semantically search a memory collection (namespace) and return the most relevant stored entries. The retrieval half of RAG, backed by Cloudflare Vectorize.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Natural-language query; returns the most semantically similar stored entries. | |
| top_k | No | How many results to return (default 5). | |
| namespace | Yes | The namespace to search within (the same key used on upsert). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It mentions semantic search and the underlying technology (Cloudflare Vectorize) but fails to clarify whether the search is read-only, what happens if the namespace does not exist, or any side effects. This is a significant gap.
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?
Two sentences, front-loaded with purpose, no wasted words. Every sentence provides value.
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?
For a simple retrieval tool with no output schema and no annotations, the description covers basic functionality but omits important context such as the meaning of 'namespace', error handling, or relationship to 'memory_upsert'. It is adequate but not thorough.
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 input schema already provides 100% coverage with clear descriptions for all three parameters. The description does not add significant new details beyond the schema; it restates the purpose. Baseline score of 3 is appropriate.
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?
Description clearly states the tool performs semantic search over a memory collection, using a verb ('search') and a specific resource ('memory collection/namespace'). It also places the tool in context as 'the retrieval half of RAG', which distinguishes it from its sibling 'memory_upsert'.
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?
The description implies usage for retrieval in a RAG pipeline, providing clear context. However, it does not explicitly state when to avoid using this tool or suggest alternatives, though the single sibling makes the distinction obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
memory_upsertAInspect
Store a piece of text in a persistent, searchable memory collection (namespace). Embedded and indexed on Cloudflare Vectorize for later semantic recall.
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | Optional stable id to update an existing entry; auto-generated if omitted. | |
| text | Yes | The text/content to store and make searchable. | |
| namespace | Yes | Your collection key — groups and isolates your memories. Treat it like a secret: anyone with it can read/write this collection. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose all behavioral traits. It mentions embedding and indexing but fails to clarify that the tool performs an upsert (update if id exists, insert otherwise). This is critical for a tool named 'memory_upsert' and is missing from the description.
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 uses two concise sentences with no wasted words. The key information (store, persistent, searchable, namespace, embedding) is front-loaded effectively.
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?
For a tool with 3 parameters and no output schema, the description is mostly complete but lacks explanation of the upsert behavior (overwrite vs create). Given the complexity and available schema, it misses an important behavioral detail.
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?
Schema coverage is 100%, with each parameter having a description. The description adds no additional parameter details beyond the schema. Baseline 3 is appropriate since the schema already fully documents parameters.
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 tool stores text in a persistent, searchable memory collection using Cloudflare Vectorize, with the verb 'store' and resource 'text in memory collection'. It distinguishes from the sibling tool 'memory_search' which performs retrieval.
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?
The description implies usage for storing content to be recalled semantically but does not provide explicit when-to-use, when-not-to-use, or comparisons with alternatives like memory_search. The warning about namespace secrecy is helpful but incomplete for decision making.
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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