update_conversationUpdate conversation status, priority, assignment, or tags.
| Name | Required | Description | Default |
|---|---|---|---|
| tags | No | Set tags | |
| status | No | ||
| priority | No | ||
| sentiment | No | ||
| assignedTo | No | Assign to team member ID | |
| conversationId | Yes | Conversation ID |
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?
The description does not contradict the annotations (readOnlyHint=false, openWorldHint=true, destructiveHint=false). The annotations already convey that this is a write operation, and the description adds a list of updatable fields, which is more parameter-related than behavioral. No additional behavioral context is provided, such as partial update semantics, side effects, or return behavior, but the annotations cover basic safety traits.
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, focused sentence that immediately states the action and the affected fields. It contains no fluff or unnecessary detail, making it highly concise. The omission of sentiment is a completeness issue, not a conciseness issue.
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 mutation tool with 6 parameters and no output schema, this description is too brief. It omits the sentiment parameter, doesn't clarify whether updates are partial or full replacements, and provides no behavioral or return-value context. An agent relying solely on this description would be under-informed about the full scope of the 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?
Schema description coverage is 50%. The description mentions status, priority, assignment, and tags, but omits sentiment and conversationId. It adds some context by echoing these field names, but doesn't explain their value ranges (enums are already in the schema) or compensate for the missing sentiment description. This is similar to a moderate coverage scenario where the description provides marginal value but not full compensation.
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 'update' with the resource 'conversation' and lists the updatable fields (status, priority, assignment, tags). This clearly distinguishes it from read-only siblings like get_conversation and list_conversations, even though it doesn't explicitly name alternatives. The purpose is unambiguous.
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?
No guidance is provided on when to use this tool versus alternatives. There's no mention of exclusions, prerequisites, or comparisons to sibling tools like reply_to_conversation or get_conversation. The usage context is only implied by the verb 'update'.
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.
With 148 tools, there is significant overlap. For example, generate_content, publish_ai, generate_post_bundle, and request_project_content all generate content; get_analytics, get_unified_analytics, get_post_analytics, get_ad_performance, and get_unified_ad_report all fetch performance metrics; and list_inbox vs list_conversations blur comment and conversation management. Descriptions help, but boundaries between tools are often unclear.
Most tools follow a verb_noun pattern (e.g., list_teams, create_goal, delete_post), but there are notable deviations: create_library_item vs save_to_library, publish_content vs publish_ai, schedule_content vs schedule_content_advanced, and connect_platform vs connect_connector. Mixed prefixes like 'autopilot_', 'check_', and 'get_' are fine, but overlapping verbs and a hyphen in 'connect_linkedin-page' reduce consistency.
148 tools is extreme for any server. Even for a broad social media management platform, this is far beyond what an agent can effectively navigate. The count is unwieldy and suggests the surface should be split into multiple focused servers (publishing, analytics, connectors, workflows, etc.).
The core social publishing workflow is well covered (create, schedule, publish, edit, delete, retry), and there are extensive features for analytics, workflows, connectors, and AI agents. However, some resources have CRUD gaps: no update/delete for brand voices, no delete_project, no update/delete for Product Hunt goals, and no explicit get_workflow. These are workable but notable omissions.