Mycelium
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| DATAHUB_GMS_URL | No | The URL of the DataHub GMS endpoint. | |
| DEEPSEEK_API_KEY | No | API key for DeepSeek LLM (optional, for narrative). | |
| MYCELIUM_BACKEND | No | The backend to use: 'snapshot' (offline) or 'live' (production). Default is 'snapshot'. | snapshot |
| ANTHROPIC_API_KEY | No | API key for Anthropic Claude LLM (optional, for narrative). | |
| DATAHUB_GMS_TOKEN | No | The authentication token for DataHub. | |
| MYCELIUM_WRITEBACK_ENABLED | No | Enable writeback to DataHub (true/false). Default is false. | false |
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
} |
| logging | {} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| extensions | {
"io.modelcontextprotocol/ui": {}
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| mycelium_triageA | Triage a data incident end to end: cause, severity, blast radius, owner, next actions. Use this instead of What comes back is computed from DataHub, not inferred: the severity (P1-P4) with the inputs
that produced it and the rule that combined them, the assets downstream and how many of them a
person actually watches, the cause with its lineage path and evidence, the owner or the
escalation path when there is no owner, a timeline, ranked next actions, and -- importantly --
a Three things worth knowing before you act on it:
|
| mycelium_askA | Route a question and answer it if it does not need deliberation. Start here for every question. Lookups ("who owns X", "is X fresh", "what columns does X
have") come back answered, with zero model calls and no context spent. Anything genuinely
open-ended comes back marked for investigation, at which point use If the router escalated because the entity was ambiguous, the reasons say so -- resolve that with the user before investigating, rather than investigating the wrong asset thoroughly. |
| mycelium_forageA | Walk the lineage graph on a budget and return a ranked shortlist of what to look at. This is not a lineage dump. Every candidate is fetched at forest resolution only -- the walk is deciding where to read, not reading. Results come back best-first, each with the trail that reached it. Treat the ranking as a hypothesis. Investigate the top three; note the rest without reading them. An empty result is a real finding: it means the cause is outside the hop budget or the direction was wrong, not that there is no cause. |
| mycelium_zoomA | Materialise one entity at a chosen level of detail, and admit it to working memory. Default to At most four entities sit at leaf resolution at once. If you need a fifth, the least-used one is demoted to make room; that is the mechanism working, not a failure. If the response says columns were truncated, quote the stated count rather than concluding a column is absent. |
| mycelium_stepA | Advance the reasoning loop one step: decay activation, demote, prune. Call this between phases of a long investigation. Anything you have not referenced for
three steps leaves working memory -- and is retrievable with |
| mycelium_recallA | Bring something back that was pruned. Nothing was ever destroyed, only set down. Cheaper than re-foraging or re-zooming, so prefer this over hoarding context "in case" -- hoarding is what degrades your own reasoning. |
| mycelium_reinforceA | Lay pheromone on the trails that reached these entities. This is the step that makes the next investigation cheaper. Be honest about the list: reinforcing something you merely looked at teaches the network a wrong path, and it will keep sending agents there for weeks. An empty reinforcement is better than a padded one. |
| mycelium_statsB | What the network has learned, and what is currently in mind. Useful mid-investigation to see how much of the budget is left, and after one to see which paths the catalogue's traffic has carved. |
| mycelium_writebackA | Write the learned hot paths back into DataHub as a knowledge-base document. Turns a private optimisation into shared context: the catalogue that fed the agent gets
better because the agent used it. Requires |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
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