validate_imeiValidate an IMEI (15 digits, with Luhn check) or IMEISV (16 digits, no check). Keyless/offline. Returns validity and the parts: TAC (Type Allocation Code, first 8), serial number, and check digit. Spaces/dashes ignored. Validates the number, not the device.
| Name | Required | Description | Default |
|---|---|---|---|
| imei | Yes | A 15-digit IMEI or 16-digit IMEISV, e.g. "490154203237518". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive. The description adds key context: 'Keyless/offline' (no side effects), 'Spaces/dashes ignored' (input processing), and 'Validates the number, not the device' (scope limitation).
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 efficient sentences, front-loaded with the core purpose. Every sentence adds value: validation method, input handling, output details, and scope clarification.
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 validation tool with one parameter and no output schema, the description fully explains input handling, validation logic, and the components of the result (validity, TAC, serial, check digit). It also clarifies what the tool does not do.
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% for the single parameter, but the description adds useful detail: allowed formats (15/16 digits), that spaces/dashes are ignored, and what the validation checks (Luhn). This goes beyond the schema's basic description.
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 it validates IMEI/IMEISV with specific validation rules (Luhn check for IMEI) and explains what it returns (validity, TAC, serial, check digit). It distinguishes from device validation by explicitly saying 'Validates the number, not the device.'
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 includes 'Keyless/offline' indicating no authentication needed, and clarifies it does not validate the device. However, it does not explicitly differentiate from sibling tool 'imei_check_digit', though the purpose is distinct enough.
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 tools have unclear boundaries: ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, and deep_research, validate_claim, discover_tools, and suggest_questions all overlap with the ask_pipeworx family. The prediction-market cluster also has six tools whose distinctions require careful reading, making mis-selection likely.
Most names are lowercase snake_case and reasonably descriptive, but no consistent verb_noun pattern holds across the set. entity_profile, recent_alerts, and pipeworx_trending are noun phrases, while compare_entities, resolve_entity, and validate_imei are verbs, and the useful ask_pipeworx_* and polymarket_* prefixes are not applied server-wide.
33 tools is too many for an agent to navigate efficiently, especially since the underlying data surface is already hidden behind ask_pipeworx and dozens more tools. The set spans data research, prediction markets, memory, subscriptions, IMEI validation, dependency scanning, and llms.txt generation, making it feel like a grab bag rather than a focused server.
The main query/verify/research/monitor workflow is covered well: ask, grounded, deep research, claim validation, entity profiles, comparisons, subscriptions, and memory all exist, so common paths have few dead ends. However, the set is not a single coherent domain, and there is no direct fetch/read-record tool or prediction-market execution tool, leaving some reasonable follow-up actions implicit.