relative_timeHumanize a date/time relative to now (or a given from): "2 hours ago", "in 3 days". Keyless, offline.
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes | The target date/time (ISO-8601 or Unix timestamp). | |
| from | No | Optional reference time (default: now). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "date": "2026-07-01T12:00:00Z"
+ },
+ {
+ "date": "1704067200",
+ "from": "2024-01-01T00:00:00Z"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly, openWorld, idempotent, and non-destructive behavior. The description adds 'keyless, offline', disclosing that no internet or API key is required. This is useful context beyond annotations.
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 efficient sentence with a key phrase. It is front-loaded, contains no fluff, and every word contributes to understanding. Ideal 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 tool with 2 well-documented parameters, no output schema, and comprehensive annotations, the description covers all necessary context: purpose, usage (examples), and behavior (keyless, offline). Complete and self-contained.
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 descriptions for both parameters. The description adds meaning by explaining the output format via examples and clarifying the default behavior for the 'from' parameter. This goes beyond the schema alone.
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 humanizes a date/time relative to now or a given 'from', with examples like '2 hours ago' and 'in 3 days'. It also distinguishes itself by noting 'Keyless, offline', setting it apart from sibling tools like from_timestamp or deep_research.
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 relative time strings but does not explicitly compare with alternatives or provide when-to-use/when-not-to-use guidance. Sibling tools like from_timestamp and to_timestamp have overlapping functionality, but no differentiation is offered.
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.
Many tools occupy heavily overlapping territory: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, bet_research, validate_claim, and even entity_profile/compare_entities all route questions to similar underlying data and could easily be misselected. The Polymarket suite adds another cluster of near-synonymous tools. Descriptions are detailed, but the boundaries require careful reading to keep straight.
Tool names mix imperative verbs (remember, subscribe, resolve_entity, validate_claim), noun-phrase descriptors (entity_profile, recent_changes, polymarket_edges), and time-utility names (now, from_timestamp, to_timestamp, relative_time). All-lowercase snake_case is consistent, but there is no unified verb_noun or domain-prefix pattern across the set.
35 tools is heavy and exceeds the comfortable 3-15 range, and most of them are unrelated to the server name 'Timestamp,' which adds confusion. The broad Pipeworx data scope justifies more than a tiny utility server, but the count still feels overstuffed and will burden tool selection.
For the broad data-research and prediction-market domain the set actually covers, the lifecycle is fairly complete: query, deep research, entity profiles, comparisons, claim validation, subscription management, and memory storage all have working operations. However, the surface is sprawling and includes one-off tools like generate_llms_txt and scan_dependency that do not fit any coherent domain, making completeness hard to assess and leaving a fuzzy, fragmented impression.