memory_store
Store session memory note.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | ||
| value | Yes | ||
| session_id | Yes |
Store session memory note.
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | ||
| value | Yes | ||
| session_id | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only says 'store', which implies a mutation, but does not mention whether it overwrites existing values, if it is idempotent, requires any authentication, or what the response looks like. This is a significant gap for a write operation.
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, short sentence that is immediately clear and front-loaded. It contains no unnecessary words or repetition. It is appropriately concise for a tool with a simple purpose, even if that conciseness trades off completeness.
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 3-parameter tool with no output schema and no annotations, the description should still provide behavioral context and return information. It does not mention whether the store is an upsert, how to retrieve it (though memory_search exists), or any error conditions. The agent lacks essential information to invoke this tool properly in complex scenarios.
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 description coverage is 0%, so the description must compensate for all three parameters. It does not explicitly describe any of the parameters; the names (session_id, key, value) are somewhat self-explanatory, but the description does not clarify format, uniqueness, or semantics beyond the obvious. The agent has to guess at the intended usage of each field.
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 what the tool does: it stores a memory note in a session. The verb 'store' and resource 'session memory note' are specific enough to distinguish from the sibling memory_search, which retrieves. However, it lacks details about whether this is an insert/update or how it differs from other potential memory operations.
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?
There is no explicit guidance on when to use this tool versus alternatives. The sibling memory_search is different, but the description does not mention it or explain usage context, prerequisites, or when not to use this tool. The agent is left to infer based on the name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Multiple tools have overlapping or identical purposes, such as ocr_url and ocr_image (both OCR from an image URL), compare_texts and text_diff (both compare or diff texts), extract_url and read_url (both extract webpage content), and content_hash and hash_text (both compute hashes). The boundaries between these tools are unclear, causing a high risk of misselection.
Naming conventions are mixed. Many tools use verb_noun (extract_url, validate_email), but others use noun_verb (language_detect, html_clean), single words (advisor, crawl, retrieve), or noun_noun (job_status, page_metadata). This inconsistency makes it harder to predict tool names.
With 100 tools, the server is extremely over-scoped for a generic agent toolkit. While some tools are distinct and useful, the sheer number does not align with a focused purpose; many tools are redundant or highly specialized, and the count exceeds what is typically manageable for an agent to reason about.
The toolkit covers a broad range of utilities including extraction, validation, processing, research, memory, and orchestration. However, there are no CRUD tools for creating/updating/deleting resources, no database or file system operations, and no integration beyond web/API basics. This leaves significant gaps for agents that need general lifecycle management, though it does handle many common tasks.