spell_natoSpell text using the NATO phonetic alphabet (A -> "Alfa", B -> "Bravo"…). Great for reading codes/confirmation numbers aloud. Keyless, offline.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to spell. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "text": "ALPHA123"
+ },
+ {
+ "text": "Confirmation code ABC456XYZ"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds 'keyless, offline,' which provides additional behavioral context about operation without keys and local execution. It does not explain the output format but it is implied by the NATO spelling.
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, front-loaded with the primary action and use case. Every word adds value; no superfluous information.
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 transformation tool with one string parameter, no output schema, and comprehensive annotations, the description covers purpose, usage, and behavioral traits (keyless, offline) completely. No gaps are apparent.
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%, so baseline is 3. The description adds meaning by specifying 'NATO phonetic alphabet,' which clarifies how the input text is transformed. This goes beyond the schema's property description 'The text to spell.'
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 the action (spell), resource (text), and method (NATO phonetic alphabet) with an example. It distinguishes itself from siblings like morse_encode by specifying a different encoding scheme.
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?
It provides a clear use case ('Great for reading codes/confirmation numbers aloud') and mentions keyless, offline operation, which implies when to use. However, it does not explicitly state when not to use or compare with alternatives like Morse code.
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.
Multiple tools blur together: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-variants, and the six polymarket_* tools plus bet_research heavily overlap in purpose. Descriptions are detailed and cross-reference each other, but an agent must read very long definitions to avoid misselection.
The set is consistently snake_case with helpful domain prefixes like ask_pipeworx, polymarket_*, and scan_*. Deviations such as deep_research, entity_profile, recent_alerts, and the bare verbs remember/recall/forget break a strict verb_noun pattern but remain predictable.
34 tools is heavy for one server, and several groups could plausibly be consolidated. However, the platform spans data lookup, research, prediction markets, memory, subscriptions, and utilities, so the breadth partially justifies the count.
The surface covers lookup, grounded verification, deep research, entity comparison, claim validation, prediction-market analysis, memory CRUD, subscription lifecycle, and discovery. There are no obvious dead ends, and gaps are minor or workaroundable.