observe-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| OBSERVE_TOKEN | Yes | Observe bearer token. Generate at: Observe UI → Settings → API Tokens | |
| OBSERVE_CUSTOMER_ID | Yes | Your Observe tenant ID (numeric). Found in your Observe URL: https://<id>.observeinc.com | |
| OBSERVE_DEFAULT_DATASET | No | Default dataset path used when dataset_path is not passed |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| observe_queryB | Run a raw OPAL pipeline query against Observe. Returns up to rowCount rows. |
| search_service_logsB | Search Observe logs for a specific service. Convenience wrapper around OPAL. |
| search_entity_logsA | Search Observe logs filtered by a specific field value — useful for correlating logs to a particular entity such as a user ID, request ID, device ID, or any other identifier. |
| get_dataset_schemaA | Return cached field names, example values, and suggested OPAL filter snippets for a dataset. Instant — no API call. Call this before writing any OPAL query. Generate the cache by running the discover script externally. |
| inspect_datasetA | Sample a dataset to discover available field names and example values. Call this before writing OPAL queries so you know exactly which fields to filter on. |
| list_datasetsA | List all datasets in Observe, optionally filtered by name keyword. Use this to discover dataset paths before running queries. |
| observe_docsA | Show OPAL query language reference and examples for writing Observe queries. Call this before writing OPAL pipelines. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 7 tools
Tools have distinct purposes overall, but get_dataset_schema and inspect_dataset both deal with field discovery, potentially causing confusion. search_entity_logs and search_service_logs are similarly structured but target different scopes.
Most tools use snake_case with verb-first naming, but observe_docs and observe_query break the pattern by using the server name prefix instead of a verb. get_dataset_schema and inspect_dataset share 'dataset' but use different verbs, causing slight inconsistency.
Seven tools is well-scoped for a query-focused Observe MCP server, covering dataset discovery, schema inspection, query execution, and log searching without being overwhelming.
The tool surface covers core querying tasks: listing datasets, inspecting schemas, running queries, and searching logs. Missing might be dataset creation or alert management, but these are outside the apparent query-focused scope.