monday_list_boardsList all boards in your account. Returns board ID, name, state, and item count to discover available boards.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of boards to return (default 20, max 50) | |
| _apiKey | Yes | Monday.com API token |
| Name | Required | Description | Default |
|---|---|---|---|
| boards | Yes | List of boards in the account |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "boards": {
+ "description": "List of boards in the account",
+ "items": {
+ "properties": {
+ "board_kind": {
+ "description": "Kind of board",
+ "type": "string"
+ },
+ "id": {
+ "description": "Board ID",
+ "type": "string"
+ },
+ "items_count": {
+ "description": "Number of items in board",
+ "type": "number"
+ },
+ "name": {
+ "description": "Board name",
+ "type": "string"
+ },
+ "state": {
+ "description": "Board state",
+ "type": "string"
+ },
+ "updated_at": {
+ "description": "Last update timestamp",
+ "type": "string"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "boards"
+ ],
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {
+ "_apiKey": "your-monday-api-key"
+ },
+ {
+ "_apiKey": "your-monday-api-key",
+ "limit": 50
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false. The description adds value by disclosing the returned fields, helping agents anticipate the response shape. It does not contradict the 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?
The description is a single concise sentence that front-loads the main action and adds useful return-field details. No filler words or redundant 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 list tool with full schema coverage, an output schema, and strong annotations, the description covers purpose, returns, and context. The only minor gap is that 'all boards' is technically constrained by the limit parameter, but the schema clarifies this, so it's not a significant omission.
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%, with both _apiKey and limit fully described. The description adds no parameter-specific guidance, but none is needed given the schema's completeness, so baseline 3 is appropriate.
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 lists boards and specifies the return fields (board ID, name, state, item count), distinguishing it from siblings like monday_get_board that target specific boards. The phrase 'to discover available boards' reinforces its discovery purpose.
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 discovering available boards, providing a clear context. However, it does not explicitly mention alternatives or when not to use this tool, so it lacks explicit exclusions. The context is still sufficient.
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 set has several overlapping tool clusters. ask_pipeworx and ask_pipeworx_beta are explicitly identical in behavior, ai_visibility_check and scan_competitor_ai_presence overlap heavily, and polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, and polymarket_fill_risk all target opportunity-finding/fill-checking on prediction markets. While individual descriptions are detailed, an agent could easily pick the wrong tool among these near-duplicates.
Naming is inconsistent across the surface. The monday_* and polymarket_* prefixes are consistent within their subgroups, and ask_pipeworx_* forms a family, but the rest mix verb_phrase (validate_claim, compare_entities, discover_tools), noun_phrase (entity_profile, recent_changes, suggest_questions), and bare verbs (remember, forget, recall) with no unifying pattern. This makes it hard to predict tool names.
At 36 tools, the surface is overloaded. The Monday.com integration alone only needs 5 tools, and the remaining 31 are a sprawling research/meta-toolkit. Many of these could be consolidated (e.g., ask_pipeworx and ask_pipeworx_beta, or the several polymarket scanners), so the count feels inflated beyond what the server's core purpose requires.
The data-research and monitoring side is thorough, covering querying, grounding, comparison, profiling, entity resolution, subscriptions, memory, and feedback. However, the Monday.com integration is incomplete: it offers create/list/get/search for items but no update or delete operations, and no board creation or modification. This leaves the Monday workflow with dead ends.