A personal RAG database you build from chat, so AI creates work that sounds like you.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
get_documentcontribute_to_workspacelist_workspacesadd_knowledge2 fields changedInput schema / properties / provenance / descriptionPrevious value: -"Where this content originates. personal = the user's own thinking. organisational = from their organisation (e.g. team docs, internal). external = from outside (articles, reports, third-party authors). Default: personal."New value: +"Where this content originates. personal = the user's own thinking. employer = from the user's own company/employer (team docs, internal materials, colleagues). client = from or about a specific client (their voice, their deliverables, their brief). external = from outside (articles, reports, third-party authors). organisational is a legacy value being split into employer/client; prefer the more specific value. Default: personal."Input schema / properties / provenance / enumPrevious value: -[
- "personal",
- "organisational",
- "external"
-]New value: +[
+ "personal",
+ "organisational",
+ "employer",
+ "client",
+ "external"
+]update_knowledge2 fields changedInput schema / properties / provenance / descriptionPrevious value: -"Update provenance: personal / organisational / external"New value: +"Update provenance: personal / employer / client / external (organisational is legacy)"Input schema / properties / provenance / enumPrevious value: -[
- "personal",
- "organisational",
- "external"
-]New value: +[
+ "personal",
+ "organisational",
+ "employer",
+ "client",
+ "external"
+]add_from_url1 field changedInput schema / properties / notes / descriptionPrevious value: -"Why you're ingesting this — adds context to what gets stored"New value: +"Why you're ingesting this. Adds context to what gets stored."add_knowledge2 fields changedInput schema / properties / content / descriptionPrevious value: -"The actual content — write it clearly, in the user's voice"New value: +"The actual content. Write it clearly, in the user's voice."Input schema / properties / type / descriptionPrevious value: -"Type for this item. Common types:\n • skill — how you express something. Your LinkedIn voice, your email style, your proposal structure. The craft layer that shapes knowledge into output.\n • principle — repeating values, rules, guidelines you apply consistently.\n • knowledge — expertise areas, domain knowledge, what you know deeply.\n • idea — concepts, hypotheses, things you're exploring.\n • voice — writing style, tone, how you communicate.\n • brand — visual preferences, aesthetic principles, brand rules.\n • template — reusable structures, formats, scaffolding.\n • resource — links, documents, references you trust.\n • reference-record — created via add_reference_record after a creation task. Do not store directly.\n • meta-principle — surfaced by find_patterns after enough reference records exist.\nOr any custom type already in the user's schema."New value: +"Type for this item. Common types:\n • skill: how you express something. Your LinkedIn voice, your email style, your proposal structure. The craft layer that shapes knowledge into output.\n • principle: repeating values, rules, guidelines you apply consistently.\n • knowledge: expertise areas, domain knowledge, what you know deeply.\n • idea: concepts, hypotheses, things you're exploring.\n • voice: writing style, tone, how you communicate.\n • brand: visual preferences, aesthetic principles, brand rules.\n • template: reusable structures, formats, scaffolding.\n • resource: links, documents, references you trust.\n • reference-record: created via add_reference_record after a creation task. Do not store directly.\n • meta-principle: surfaced by find_patterns after enough reference records exist.\nOr any custom type already in the user's schema."add_reference_record2 fields changedInput schema / properties / nuance / descriptionPrevious value: -"What was different or adapted in THIS specific case — the lesson worth keeping"New value: +"What was different or adapted in THIS specific case. The lesson worth keeping."Input schema / properties / tags / descriptionPrevious value: -"Tags — inherit from the knowledge and skill plus any case-specific tags"New value: +"Tags: inherit from the knowledge and skill plus any case-specific tags."add_schema_type2 fields changedInput schema / properties / description / descriptionPrevious value: -"What this type stores — one clear sentence"New value: +"What this type stores. One clear sentence."Input schema / properties / name / descriptionPrevious value: -"Type name — lowercase, short, e.g. \"case-study\" or \"objection\""New value: +"Type name. Lowercase and short, e.g. \"case-study\" or \"objection\"."search_for_creation1 field changedInput schema / properties / query / descriptionPrevious value: -"What you're looking for — natural language, the task or topic at hand"New value: +"What you're looking for. Natural language, the task or topic at hand."search_twin1 field changedInput schema / properties / query / descriptionPrevious value: -"What you're looking for — natural language"New value: +"What you're looking for. Natural language."add_documentadd_from_urladd_knowledgeadd_reference_recordadd_schema_typeadd_voice_notedelete_knowledgefind_patternsget_by_tagget_by_typeget_schemaget_sourcesget_welcomelist_recentsearch_for_creationsearch_twinsynthesiseupdate_knowledgeupdate_schema_typeClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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 as io.github.acme/server also 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.json on 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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Stop re-explaining yourself to AI. Save knowledge once, use it in every conversation.
Loads your personal writing voice into any AI and scores how closely a draft matches it.
Personal wiki and memory layer for AI assistants. Persistent, structured memory across sessions.
AI memory layer — one shared, persistent memory across every AI tool you connect.
Add one secure layer between your agents and this server.
Each tool targets a distinct action or resource: adding different input types (document, URL, knowledge, etc.), retrieving by various criteria (tag, type, document ID), and managing workspace. The detailed descriptions make the boundaries clear, with no overlapping tools.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., add_document, get_by_tag, search_twin). Even 'synthesise' is a verb, and the pattern is maintained across 22 tools, making it predictable for an agent.
22 tools is above the typical 3-15 ideal but each one serves a specific and necessary function within the knowledge management domain. The count is well-justified, covering ingestion, retrieval, analysis, and sharing, though slightly on the higher side.
The tool set covers the core lifecycle of knowledge management: add (multiple source types), update, delete, search, retrieve, and share. Minor gaps exist, such as no dedicated tool to delete ingested sources (documents/URLs) independently of knowledge items, but these can be worked around.
