to_timestampConvert an ISO-8601 (or other parseable) date string to a Unix timestamp (seconds + milliseconds). Keyless, offline.
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes | A date/time string, e.g. "2026-07-01T12:00:00Z". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "date": "2026-07-01T12:00:00Z"
+ },
+ {
+ "date": "2024-01-01"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide strong safety signals (readOnly, idempotent). The description adds meaningful behavioral context ('keyless, offline') beyond the annotations, but does not detail edge cases or limitations. This is good but not exhaustive.
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 extremely concise—two short sentences—with no filler. It front-loads the core action and outcome, making it efficient for agents to parse.
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 one parameter, no output schema, and no nested objects, the description covers all essential information: input format, output format, and operational characteristics. No gaps are present.
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 a clear description of the 'date' parameter. The description adds value by specifying 'ISO-8601 (or other parseable)', indicating flexibility beyond the schema's example, which enhances understanding.
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 uses a specific verb ('Convert'), names the input ('ISO-8601 or other parseable date string'), and clearly states the output ('Unix timestamp with seconds and milliseconds'). It also distinguishes itself by noting 'Keyless, offline', which sets it apart from siblings like 'from_timestamp'.
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 implicitly guides usage by stating 'Keyless, offline', indicating it works without authentication or network. However, it does not explicitly contrast with similar tools (e.g., from_timestamp) or provide when-not-to-use scenarios.
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.