HumanMirror Oracle
Server Details
Structured analysis API and remote MCP tool for text, JSON records and numeric series.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Available Tools
1 toolhumanmirror_oracleAInspect
Use HumanMirror Oracle when an application or AI agent needs structured analysis of text, JSON objects, record arrays, or numeric series: data-quality checks, trends, anomalies, summaries, and recommendations. Do not use it as a substitute for professional advice in high-impact decisions. One successful call consumes 1 Oracle credit.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Text, JSON object, array of records, or numeric series to analyze. | |
| context | No | Optional context that helps interpret the input. | |
| objective | No | Optional analysis objective, for example: detect anomalies, check data quality, identify trends, or summarize structure. |
Output Schema
| Name | Required | Description |
|---|---|---|
| ok | Yes | |
| error | No | |
| usage | No | |
| result | No | |
| version | Yes | |
| request_id | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide read-only/destructive/idempotent hints. The description goes beyond them by disclosing that one successful call consumes 1 Oracle credit and by adding a high-impact advisory limitation. It does not detail determinism or failure behavior, but the annotations lower the burden and the added cost/limitation context is valuable.
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 two sentences with no filler. It front-loads the core use case, then adds the critical limitation and credit cost, making every sentence informative and structurally efficient.
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?
Given the output schema exists and all parameters are documented, the description covers selection and invocation needs: input shapes, analysis types, usage boundary, and cost. It does not explain how to phrase objectives or context in depth, but the schema already provides examples and the description is otherwise complete enough for correct use.
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%, so input, context, and objective are already documented in the schema. The description restates input types and lists analysis objectives that are nearly identical to the schema's examples, adding only minimal new meaning like 'recommendations'. This satisfies the baseline but does not substantially augment the schema.
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 names a specific capability: structured analysis of text, JSON objects, record arrays, and numeric series, with concrete output types like data-quality checks, trends, anomalies, summaries, and recommendations. This makes the tool's purpose clear and distinct even without siblings.
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?
It explicitly states when to use the tool ('when an application or AI agent needs structured analysis') and gives a clear exclusion boundary ('Do not use it as a substitute for professional advice in high-impact decisions'). No sibling tools exist, so additional alternative routing is unnecessary.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
1 tool update
- First observed
humanmirror_oracle
Frequently Asked Questions
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Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as
io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such asio.github.acme/serveralso needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at
/.well-known/glama.jsonon the same origin as the connector, then choose Check HTTP challenge.DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
With only one tool, there is no possibility of selecting between overlapping tools. The purpose is clearly described, so an agent cannot misselect.
The single name 'humanmirror_oracle' is clear, readable snake_case, but no verb_noun pattern can be established from one tool. There is no inconsistency to penalize heavily.
One tool is far too few for the advertised breadth: text, JSON objects, record arrays, numeric series, quality checks, trends, anomalies, summaries, and recommendations. The broad scope suggests several specialized tools or clear modes would be more appropriate.
The single tool claims to cover all the analysis operations listed, so there are no obvious missing analysis functions. However, it is an opaque all-in-one endpoint with no finer-grained surface, which limits granular completeness.