front-asset-intel-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 |
|---|---|
| get_asset_summaryA | Return the precomputed rubric-style JSON summary for an asset. For tables and analyst-agent routing, prefer agent_display.score_display, agent_display.decision_label, underwriting_status, execution_automation_status, primary_blockers, and next_action over the legacy rubric.score/decision_class fields. The payload also includes per-rubric dimension score, score band, status, evidence state, and evidence pointers. |
| get_asset_researchA | Return the full precomputed Markdown research report for an asset. For simple-token assets, the MCP response prepends the validated simple_token_return_estimate from summary JSON so research callers get organic ROI, estimated points ROI, expected-loss bands, and risk-adjusted ROI without a second tool call. Use this when the rubric answer needs source context or audit detail. |
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 2 tools
The two tools have clearly distinct purposes: one returns a detailed Markdown research report, the other returns a rubric-style JSON summary. There is no overlap in their outputs or intended use cases.
Both tools follow a consistent 'get_asset_<noun>' pattern, using the same verb 'get' and clearly differentiating the resource object ('research' vs 'summary').
With only two tools, the server feels under-scoped for an asset intelligence platform. Users likely need additional capabilities like searching, listing, or comparing assets, which are absent.
The tool set covers retrieval of two specific asset reports but lacks any CRUD operations, listing, search, or ability to handle multiple assets. This represents significant gaps in functionality for a dedicated asset intelligence server.