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Glama

Phone

by io.github.pipeworx-io

Validate Phone

validate_phone
Read-onlyIdempotent

Validate & parse a phone number against the E.164 / ITU calling-code plan (keyless, offline). Detects the country, normalizes to E.164, and checks the national-number length is plausible. Pass an international number (e.g. "+33 1 23 45 67 89") OR a national number plus a country ISO code (e.g. phone="020 7946 0958", country="GB"). Does NOT determine carrier or mobile-vs-landline (that needs a keyed HLR lookup).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
phoneYesThe phone number to validate (international "+.." form, or national form with `country`).
countryNoOptional ISO 3166 alpha-2 country code (e.g. "US", "GB", "DE") used when `phone` is in national (non-+) form.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "phone": "+33 1 23 45 67 89"
+  },
+  {
+    "country": "GB",
+    "phone": "020 7946 0958"
+  }
+]
  2. First observed

TDQS

A4.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate read-only and idempotent behavior. The description adds valuable context: it's keyless/offline, detects country, normalizes to E.164, and checks length plausibility. It also clarifies what it does not do, which is beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise at three sentences, with no redundant information. It front-loads the core purpose, then provides usage patterns, and finally lists exclusions. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (2 params, no output schema, clear annotations), the description fully covers input requirements, constraints, and limitations. It is complete for an agent to correctly select and use the tool without confusion from sibling tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 100% coverage with clear descriptions for both parameters. The description adds meaning by providing usage examples and explaining the relationship between 'phone' and 'country', clarifying the two allowed input formats (international vs national+country).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool validates and parses phone numbers against E.164, distinguishing it from siblings like 'country_calling_codes' by specifying it checks plausibility and detects country. It also explicitly states what it does not do (carrier determination), differentiating it from other tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage modes: international number without country, or national number with country code. It also tells when not to use the tool (for carrier or line-type determination), and suggests an alternative ('keyed HLR lookup').

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Phone toolsInspect
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TDQS

A4/5.0
Disambiguation4/5

Tool purposes are mostly distinct, with detailed descriptions differentiating similar-sounding tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research. However, some overlap exists between bet_research and polymarket_* tools, requiring careful reading of descriptions to select the correct one.

Naming Consistency3/5

Tool names mix verb_noun patterns (e.g., resolve_entity, validate_claim) with noun_phrases (e.g., entity_profile, recent_alerts) and occasional inconsistencies like pipeworx_trending or search_within. While most names are readable, the lack of a uniform convention makes it harder to predict tool names.

Tool Count4/5

32 tools cover a broad domain of data retrieval, analysis, prediction markets, and system management. The count is high but reasonable given the extensive feature set, though a more focused set could improve coherence.

Completeness4/5

The tool set covers major needs: data lookup, comparison, validation, monitoring, and memory. Missing CRUD operations for external data are expected as this is a read-centric API, so gaps are minor and don't hinder common tasks.

Resources