Autocomplete
autocompleteAutocomplete company names and retrieve logo and domain information
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes |
autocompleteAutocomplete company names and retrieve logo and domain information
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -nullNew value: +{
+ "$schema": "http://json-schema.org/draft-07/schema#",
+ "additionalProperties": false,
+ "properties": {
+ "data": {}
+ },
+ "required": [
+ "data"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint and openWorldHint annotations already communicate that this is a non-destructive, open-ended lookup. The description adds that logo and domain information is returned, but does not disclose behaviors such as result limits, matching behavior, or whether the API expects partial or full company names.
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 a single concise sentence that front-loads the main purpose and immediately states the additional data returned. Every word contributes value, and there is no redundant or filler content.
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?
Given the tool's simplicity (one required parameter, no nested objects, output schema present) and the readOnly/openWorld annotations, the description covers the essentials. It could be slightly more complete by explaining the autocomplete behavior or the nature of the output, but the core calling context is sufficiently clear.
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?
The schema only defines a required 'query' string with no description, and schema coverage is 0%. The description's phrase 'autocomplete company names' indicates that the query refers to a company name or partial name, providing some semantic context, but it does not fully clarify the expected input format or matching rules.
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 states a clear action ('autocomplete company names') and specifies the retrieved data ('logo and domain information'), making the tool's purpose identifiable. It does not explicitly contrast with sibling tools like companies_search or domain_search, but its function is specific enough to be distinguished.
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 the tool is for interactive autocomplete/typeahead scenarios rather than general company search or enrichment, but it does not state any explicit when-to-use guidance or alternatives. There is no mention of when not to use this tool versus the many related sibling tools.
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 overlapping purposes: email_enrichment and person_enrichment are nearly identical, and every mpp_* tool duplicates a non-MPP tool with only payment method differences. Domain-related tools like domain_search, email_count, and domain_status also blur boundaries, making selection error-prone.
Most tools follow a readable snake_case convention with clear actions like list_, create_, get, and finder/verifier/enrichment suffixes. However, ordering is inconsistent (companies_search vs domain_search, combined_enrichment vs email_finder) and a few vague names like location and autocomplete break the pattern.
38 tools is excessive for this server's scope, especially since 10 are MPP variants that simply duplicate existing functionality with a different payment model. The core feature set could be expressed in roughly half the tools without losing capability.
The toolset covers the core Tomba workflows well: email finding, verification, enrichment, domain/company search, phone lookup, and lead management. Minor gaps exist, such as no update/delete operations for leads and no detailed lead retrieval, but agents can work around these for most prospecting tasks.