validate_postcodeValidate a UK postcode format (e.g., 'SW1A 1AA'). Returns whether it's valid and format details.
| Name | Required | Description | Default |
|---|---|---|---|
| postcode | Yes | UK postcode to validate (e.g. "SW1A 1AA"). |
| Name | Required | Description | Default |
|---|---|---|---|
| valid | Yes | Whether the postcode is valid | |
| postcode | Yes | The UK postcode that was validated |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "postcode": {
+ "description": "The UK postcode that was validated",
+ "type": "string"
+ },
+ "valid": {
+ "description": "Whether the postcode is valid",
+ "type": "boolean"
+ }
+ },
+ "required": [
+ "postcode",
+ "valid"
+ ],
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {
+ "postcode": "SW1A 1AA"
+ },
+ {
+ "postcode": "invalid"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover safety (readOnly, non-destructive, idempotent). The description adds that it returns validity and format details, which is useful but does not disclose limitations such as not checking real-world existence or handling of whitespace/case. No contradiction with annotations.
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?
A single, tightly worded sentence that immediately conveys the tool's purpose and output. No filler, front-loaded with the key action, and includes a concrete example.
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 single-parameter validator with rich annotations (readOnly, idempotent) and an output schema, the description provides enough context. It could optionally mention edge cases like case sensitivity or non-existence checks, but the tool's simplicity makes the description largely complete.
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 description coverage is 100% with an example already provided. The description repeats the postcode example, adding no significant meaning beyond the schema. Baseline 3 is appropriate since the schema fully documents the parameter.
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 the specific verb 'Validate' with a clear resource 'UK postcode format' and provides an example. It distinguishes from sibling tools like lookup_postcode, which focuses on retrieving data, and random_postcode, which generates postcodes.
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 format validation but does not explicitly state when to use this tool over alternatives such as lookup_postcode or nearest_postcodes. No exclusions or alternative tool guidance is provided, leaving usage context implicit.
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 have overlapping responsibilities: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded all serve similar lookup purposes. entity_profile, compare_entities, and recent_changes all retrieve company data. Several Polymarket tools overlap in edge detection. The large number of tools with fuzzy boundaries makes it difficult for an agent to select the correct one.
Tool names are a mix of conventions: some use verb_noun (lookup_postcode, validate_postcode, resolve_entity), others are verb_phrase (ask_pipeworx, deep_research, suggest_questions), and a few are compound (polymarket_arbitrage, scan_competitor_ai_presence). No uniform pattern, though the structure is readable.
Despite being named 'postcodes', only 4 of 35 tools are directly about postcodes. The vast majority belong to a broad data platform (Pipeworx) with specialized tools for finance, betting, news, etc. The count is excessive for a focused service, and many tools are only useful for users of that platform, leading to clutter.
For a postcode server, the tools cover basic needs (lookup, nearest, random, validate). However, the server's actual scope is much larger; within that broader scope, there are notable gaps: no general text search, no direct access to raw SEC filings, and many tools depend on paid plans or external accounts. The coverage is uneven and incomplete for a unified data platform.