parse_ulidValidate a ULID and extract its embedded creation timestamp (ISO date + epoch ms) and the random component. Keyless, offline.
| Name | Required | Description | Default |
|---|---|---|---|
| ulid | Yes | A 26-character ULID. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "ulid": "01ARZ3NDEKTSV4RRFFQ69G5FAV"
+ },
+ {
+ "ulid": "01BX5ZZKBK7XBRC2Y2HF1K9R1M"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds context that validation and extraction are performed offline, and discloses the exact outputs (timestamp and random component), which is beyond what annotations provide.
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 two sentences long, front-loading the key action and outputs. Every word contributes meaning with no redundancy or filler.
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 and no output schema, the description fully covers what the tool does, its inputs, and its context (keyless, offline). An agent has sufficient information to correctly select and invoke the tool.
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 the single parameter 'ulid' described as 'A 26-character ULID.' The description does not add new parameter details beyond what the schema already provides, so a 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?
The description explicitly states the tool validates a ULID and extracts its creation timestamp and random component, with the additional context 'Keyless, offline.' This clearly distinguishes it from its sibling tool 'generate_ulid' and provides a specific verb-resource mapping.
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 decoding ULIDs and notes it is keyless and offline, but does not explicitly state when to use it versus alternatives like generate_ulid or other tools. It lacks explicit when-not-to-use conditions or comparisons.
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 overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve as query entry points (with ask_pipeworx_beta explicitly identical to ask_pipeworx right now); polymarket_edges, polymarket_edge_tracker, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread, and bet_research form a dense prediction-market suite; ai_visibility_check and scan_competitor_ai_presence are single vs. multi variants. An agent would frequently struggle to pick the right tool without reading long descriptions.
Most tools follow a clean snake_case convention and are mostly verb_noun (generate_ulid, parse_ulid, list_subscriptions, resolve_entity, validate_claim, compare_entities), making the set predictable. Minor deviations exist (entity_profile, deep_research, bet_research, ask_pipeworx_beta) but they are still readable and don't break the overall pattern.
33 tools is well above the 25-tool threshold for a heavy surface, and the server name 'Ulid' suggests a tiny scope that wildly mismatches the actual content. While the real domain (Pipeworx data + prediction markets) is broad, the set bundles many subdomains into one server, making navigation and selection costly.
For the actual apparent domain—structured data research, entity lookups, verification, prediction-market analysis, memory, and subscriptions—coverage is strong: query, grounded query, deep research, entity profiles, comparisons, resolution, validation, discovery, alerts, and memory tools are all present. ULID functionality is minimal but sufficient (generate + parse). Minor gaps exist (e.g., no direct single-source browser beyond discover_tools) but agents can work around them.