A
licenseB
qualityA
maintenanceA meta-MCP server that manages and aggregates other MCP servers, enabling LLMs to dynamically extend their own capabilities by searching for, adding, and configuring tool servers.
16
148 PyPI
144
AGPL 3.0
rei-meta-mcp
by fc0web
rei-meta-mcp
Eine Meta-Ebene über der Gruppe von Konnektoren. Sie behandelt die einzelnen MCP-Konnektoren als Objekte und die Zugriffspfade zu den Objekten als Morphismen und verwaltet deren Struktur.
Der einzige praktische Zweck von Phase 1 ist die Kohärenzprüfung (coherence check) — das maschinelle Abgleichen, ob die Inhalte mehrerer Quellen, die auf dasselbe Objekt verweisen, übereinstimmen.
Warum ich es gebaut habe
Am 2026-08-19 wurde im Implementierungsprozess von rei-memory-mcp festgestellt, dass SEED_KERNEL um 1,677 vs 1,675 abwich und 11 Tage lang unbemerkt blieb. Es wurde nur bemerkt, weil ein Mensch zufällig beide Zahlen verglich; es gab kein maschinelles Erkennungsmittel.
Die erste Aufgabe dieses Konnektors ist es, dies maschinell zuerst zu erkennen. Details siehe docs/incident-2026-08-19.md.
Related MCP server: Code-Oracle
Bereitgestellte Tools (3)
meta_list_sources(object_name?: str)
Listet die registrierten Quellen auf und gibt für jede den Erreichbarkeitsstatus, die Anzahl und den git-HEAD-Hash (Hinweis auf die Aktualität) zurück.
meta_check_coherence(object_name: str, detail: bool = False)
Gleicht die Fingerabdrücke der Quellen ab, die auf dasselbe Objekt verweisen, und gibt eines der folgenden Urteile zurück:
verdict | Bedeutung |
| Fingerabdrücke aller erreichbaren Quellen stimmen überein |
| Uneinigkeit zwischen erreichbaren Quellen |
| Keine erreichbare Quelle |
| Nur eine erreichbare Quelle (kein Vergleich möglich) |
§4 Kernregel: „Nicht erreichbar“ ist nicht „übereinstimmend“. unreachable und single_source werden immer als Warnung angezeigt.
Mit detail=True wird die tatsächliche Liste der abweichenden IDs (maximal 100) zurückgegeben.
meta_compose(from_source: str, to_source: str)
Phase 1 führt nur den Abgleich der output_schema- / input_schema-Zeichenketten im Registry durch. Die eigentliche Schema-Inferenz erfolgt ab Phase 3.
Was Phase 1 nicht tut
Automatische Reparatur (bewusst ausgeschlossen. Die Entscheidung, welche Seite richtig ist, trifft der Mensch)
Persistenz des Prüfverlaufs (Phase 2)
Schema-Inferenz (Phase 3)
Kategorientheoretische Konstruktionen wie Funktoren, Adjungierte, Monaden (wenn sie nötig werden)
Derzeit registrierte Quellen
Siehe config/sources.example.yaml:
source | kind | Status |
|
| vollständiger Fingerabdruck ( |
|
| partieller Fingerabdruck ( |
|
| Deploy über claude.ai remote-devices kann von Python aus nicht direkt geprüft werden — immer explizit |
Installation & Verwendung
uv pip install -e ".[dev]"
cp config/sources.example.yaml config/sources.yaml # パスを埋める
uv run pytest # 全 PASS を確認
uv run rei-meta-mcp # stdio で MCP server 起動Der Registry-Pfad kann über die Umgebungsvariable REI_META_MCP_REGISTRY überschrieben werden.
Ehrlicher Umfang
Nur Erkennung, keine Reparatur — die Entscheidung bleibt beim Menschen
Die Übereinstimmung zwischen Quellen mit partiellem Fingerabdruck (mcp_stdio) bestätigt nur, dass „keine Abweichung vorliegt“, ist aber kein Beweis für „vollständige Übereinstimmung“ (es gibt Teile, die auf
content_hash-Ebene nicht sichtbar sind)Nur fünf kategorientheoretische Begriffe tragen die Last (Objekt, Morphismus, Equalizer, Komposition, Identitätsmorphismus). Andere werden vermieden
Derzeit ein Phase-1-Spike mit 3 Quellen. Eine Struktur, deren Wert mit der Anzahl der Objekte wächst
Phase 2 (Verlauf, regelmäßige Ausführung, Benachrichtigungen) und Phase 3 (Schema-Inferenz, Verallgemeinerung der Morphismen) werden erst entschieden, nachdem Phase 1 im praktischen Betrieb funktioniert hat
Lizenz
AGPL-3.0-or-later.
Verwandtes
docs/incident-2026-08-19.md— Aufzeichnung des Vorfallstests/test_incident_2026_08_19.py— Test, der den Vorfall reproduziert
Available Tools
3 toolsmeta_check_coherenceB
Check whether all sources for object_name agree.
Verdicts: coherent | divergent | unreachable | single_source. §4: unreachable/single_source are warnings, not silent success.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| detail | No | ||
| object_name | Yes |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
B3.4/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It goes beyond a simple 'check' by specifying the possible verdicts and the important warning semantics for `unreachable` and `single_source`, which is non-obvious. It does not discuss side effects, but as a read-only check that is reasonably implied.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short, front-loads the main purpose, and uses a compact verdict list plus a warning note. The only minor issue is the cryptic '§4' reference, which is terse but may require external context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The core purpose and output verdicts are covered, and the presence of an output schema reduces the need to describe return structure. However, key gaps remain: the `detail` parameter is unexplained, and there is no guidance on choosing this tool over its siblings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description clarifies that `object_name` is the object whose sources are checked, but it says nothing about the `detail` boolean parameter. Since schema description coverage is 0%, the description needed to compensate but only explains one of the two parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('Check whether all sources agree') on a specific resource (`object_name`), and enumerates the possible verdicts. It does not explicitly differentiate from sibling tools, but the verb and resource make the purpose clear enough.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The usage is implied by 'Check whether all sources agree' — the agent can infer it is for consistency checking across sources. However, there is no explicit mention of when to prefer this tool over `meta_list_sources` or `meta_compose`, nor any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
meta_composeA
Check whether the output of from_source can feed to_source.
Phase 1: declarative schema string match only.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| to_source | Yes | ||
| from_source | Yes |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
A3.6/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It clearly discloses that this is only a phase-1 declarative schema string match, which meaningfully sets expectations about the tool's limitations. This is useful context beyond what the schema alone provides.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short and front-loaded: the first sentence states the core purpose, and the second adds a critical limitation. Every sentence earns its place with no filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with two string parameters and an output schema, so return-value details are not needed. The description covers the core behavior and limitation, though it leaves some contextual ambiguity about how sources are identified and when this check is appropriate relative to sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It gives relational meaning to both parameters: 'from_source' produces output and 'to_source' receives it. However, it does not describe the expected format or examples, leaving part of the semantics implicit.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('Check whether the output... can feed...') and identifies the two resources involved. It is clear about the compositional relationship, though it does not explicitly differentiate itself from the sibling tool meta_check_coherence.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance explains when to use this tool versus meta_list_sources or meta_check_coherence. The phrase 'Phase 1: declarative schema string match only' implies a preliminary check, but it never states conditions, exclusions, or recommended alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
meta_list_sourcesA
List registered sources with current reachability and freshness.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| object_name | No |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
A3.8/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry behavioral disclosure. It conveys that the tool reports current reachability and freshness, and 'List' implies a read-only operation. However, it does not explain behavior around the optional object_name parameter, potential network/performance implications, or any side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, compact sentence that front-loads the primary action and object. Every word contributes useful information, with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple listing tool with one optional parameter and an output schema, the description provides the essential purpose and result characteristics. However, it omits parameter semantics and usage boundaries, leaving some gaps an agent would need to infer.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% for the only parameter, and the description does not mention object_name at all. The schema gives only a type, title, and default, which is insufficient for an agent to understand how filtering by object_name works or whether it is optional.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb and resource: 'List registered sources', and adds distinguishing detail with 'current reachability and freshness'. This clearly identifies the tool's purpose and sets it apart from siblings like meta_check_coherence and meta_compose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: use this tool when you need an inventory of registered sources with their current status. It does not explicitly state when not to use it or point to alternatives, but sibling names are distinct enough that there is no ambiguity.
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.
3 tool updates
v0.1.0-alpha- First observed
meta_check_coherence - First observed
meta_compose - First observed
meta_list_sources
TDQS
A3.9/5.0
Scored across 3 tools
Disambiguation5/5
Each tool has a distinct responsibility: listing registered sources, checking source agreement for an object, and validating source-to-source composability. There is minimal overlap, and the differing parameters make selection unambiguous.
Naming Consistency5/5
All tools use the `meta_` prefix with an imperative snake_case verb (`list`, `check`, `compose`), giving a predictable convention. The slight variation in whether an object follows the verb does not hurt recognizability.
Tool Count5/5
Three tools is a compact but appropriate scope for a focused metadata validation server. Each tool provides a distinct high-level capability with no redundancy.
Completeness4/5
The set covers the core discovery and validation workflows: enumerate sources, check coherence, and test composition. It is missing broader source/object management and only performs schema-string composition matching, so there are minor gaps but no dead ends for the main use case.
Maintenance
ActivityMaintained
ResponsivenessNo issues
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