Store key-value data in persistent agent memory. Price: $0.001
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | Storage key (max 256 chars) | |
| ttl | No | Time to live in hours (1-720, default: 168) | |
| value | Yes | Value to store (any JSON) |
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 provided, the description carries the full burden of behavioral disclosure. It does mention persistence and price, which are helpful, but it does not disclose potential overwrite behavior, session scoping, or any side effects beyond storage. The schema covers TTL and key length, but the description adds only persistence and cost context, which is useful but not comprehensive for an unannotated tool.
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 succinct sentence that immediately conveys the tool's purpose, with an added price note. There is no filler, and the most important information is front-loaded. It earns a perfect score for conciseness.
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 storage tool with a fully described schema, the description is nearly complete. It covers the core operation and persistence behavior, though it could be enhanced with a note on usage scenarios (e.g., when to set a TTL) or return value expectations. However, given the tool's simplicity and the schema's richness, it is adequately complete.
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 schema covers 100% of parameters with descriptions ('Storage key (max 256 chars)', 'Time to live in hours', 'Value to store (any JSON)'), so the baseline is 3. The tool description does not add any parameter-specific semantics beyond what the schema already provides, so the baseline score 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?
The description clearly states the tool's function: 'Store key-value data in persistent agent memory.' This is a specific verb+resource combination that unambiguously distinguishes it from the sibling tools, which are all research, fetch, or search operations. The purpose is immediately obvious.
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 provides clear context by mentioning 'persistent agent memory,' implying this tool should be used when data needs to be retained across operations or sessions. While there are no explicit exclusions or mention of alternatives, the context is evident for a memory storage tool given the sibling set contains no similar tools.
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.
Several tool clusters have near-overlapping purposes: fetch_webpage/fetch_webpage_pro/fetch_resilient and batch_fetch/get_contents are hard to distinguish, and answer_question/research/deep_research differ mainly in price and depth. The search_* and intel_* families are clearer, but the core fetching and research overlap creates ambiguity.
Most tools follow a verb_noun snake_case pattern (fetch_webpage, search_web, extract_data), but there are notable exceptions like domain_intel, package_intel, youtube_transcript, memory_set, and intel_company, where the prefix/suffix convention is inconsistent. Still, the naming is broadly readable.
35 tools is a large surface, far beyond the typical 3-15 range. The server covers many research verticals, but the number feels bloated, especially with multiple fetch and research variants that could be consolidated.
The tool set covers a wide range of web research needs: searching, fetching, crawling, extracting, screenshots, domain/tech/package intelligence, and market/competitive analysis. It lacks obvious lifecycle operations for monitors (list/delete/update) and memory (get/delete), but core workflows are well covered.