MCP.AI for IDE agents (Cursor, etc.): log in in the browser, copy the access token. Best: add it to this server's config as a header Authorization: Bearer <token> for a permanent, non-expiring connection. Or paste it here for a session-only login: call with { token: "" } after the user pastes, or with no args to get the link.
| Name | Required | Description | Default |
|---|---|---|---|
| token | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate non-read-only, idempotent, and non-destructive behavior. The description adds context about permanent vs. session token storage and the browser-based login flow, but does not disclose potential side effects like token overwriting or revocation.
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 paragraph that front-loads the purpose and then presents clear alternatives (Best: vs Or). It is informative without excessive verbosity, though multiple clauses make it slightly dense.
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 optional parameter, no output schema), the description adequately covers the login flow, token handling, and the two invocation patterns. It does not describe return values, but that is not critical for an authentication tool.
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?
With 0% schema description coverage, the description compensates by explaining the 'token' parameter and how to use it (or omit it). It clarifies that the token is a JWT and that calling without arguments generates an authentication link, adding meaning beyond the raw schema.
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 tool's purpose: to authenticate to the MCP.AI service for IDE agents. It specifies the resource (MCP.AI) and the action (log in, obtain token), and differentiates from siblings by focusing on the authentication flow.
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 provides explicit guidance on two usage modes: permanent config via Authorization header and session-only via token parameter. It explains when to call with no arguments (to get the link) and when to provide a token, but does not explicitly mention when not to use it or compare with the sibling 'connect' tool.
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.
The core legalone_get/list/create/update tools are clearly separated by entity and verb, so common lookups are easy to pick. However, legalone_odata_get and legalone_request duplicate the named CRUD surface, lawsuits vs litigations are near-synonyms in a legal context, and connect/toolkit_info/authenticate have overlapping connection/status duties. Descriptions help resolve most cases, but several tool pairs could still be misselected.
The majority of tools follow a consistent legalone_<verb>_<entity> pattern, which is good. But auxiliary tools break the pattern: legalone_customfield_options, legalone_odata_get, legalone_request, legalone_metadata, authenticate, connect, marketplace, report_bug, show_version, and toolkit_info use bare verbs, nouns, or platform-infrastructure names. The result is mixed conventions that remain readable but are not consistent across the whole set.
With 45 tools, this set is well beyond the 3–15 well-scoped range and above the 25-tool threshold for too many. Many tools are repetitive CRUD variants per entity, plus several infrastructure and marketplace tools that inflate the agent's selection space. A smaller entity-focused set plus one generic OData escape hatch would have covered virtually the same functionality.
Core legal entities like lawsuits, litigations, contacts, appointments, tasks, and updates have create/get/list/update coverage. However, documents, users, areas, service types, and custom field definitions lack named update/delete helpers, and no entity has a dedicated delete tool. The generic legalone_request/odata_get escape hatches fill these gaps, but the lifecycle coverage is uneven.