The AWS Knowledge MCP server is a fully managed remote Model Context Protocol server that provides real-time access to official AWS content in an LLM-compatible format. It offers structured access to AWS documentation, code samples, blog posts, What's New announcements, Well-Architected best practices, and regional availability information for AWS APIs and CloudFormation resources. Key capabilities include searching and reading documentation in markdown format, getting content recommendations, listing AWS regions, and checking regional availability for services and features.
AWS resource availability per region.
Max 10 regions; multi-region needs filters; single-region supports next_token.
Status: isAvailableIn | isNotAvailableIn | isPlannedIn | Not Found.
Response key: products | service_apis | cfn_resources.
Not for region counts/docs/vague queries -- use search_documentation / list_regions.
Filter values must EXACTLY match AWS's catalog names; guessed, partial, or pluralized names are rejected ("values in filter parameter do not exist"). If unsure of the exact name, first call once for a single region with resource_type set and NO filters to list all valid names, then re-call filtering on the exact match.
| Name | Required | Description | Default |
|---|---|---|---|
| region | No | Unused; use `regions`. | |
| filters | No | Use exact AWS product or sub-feature name. - product: 'Amazon Bedrock' (service), or sub-features like 'Comprehend Auto Scaling', 'Latency-Based Routing', 'PrivateLink Support'. When the user names a specific sub-feature, filter on the sub-feature -- do NOT generalize to the parent service ('Amazon Comprehend'); that returns availability for the wrong scope. - api: 'SdkServiceId+Operation' (e.g. 'CloudFormation+CreateStack', 'IAM+GetSSHPublicKey') or 'SdkServiceId' (e.g. 'EC2'). Use a literal '+' between service and operation -- not space, colon, or hyphen. - cfn: 'AWS::EC2::Instance', 'AWS::Lambda::Function'. Include every region the user named; don't add filters they didn't request. Values must EXACTLY match AWS's catalog (e.g. 'AWS Lambda', not 'Lambda' or 'AWS Lambda Service'). If unsure of the exact name, first call once for one region with NO filters to list valid names, then filter on the exact match. | |
| regions | No | AWS region codes (max 10). Multi-region requires `filters`; single-region supports `next_token`. | |
| next_token | No | Pagination token. Single-region, no filters only. | |
| resource_type | Yes | Required: 'product' | 'api' | 'cfn'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds rich behavioral context beyond annotations: exact-match filter requirements with error example, pagination via `next_token` (single-region/no filters), status values, and response keys. No contradiction with readOnlyHint.
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?
Bulleted, front-loaded structure with no fluff. Each sentence adds operational value—constraints, status, response keys, exclusions, and filter exact-match advice—all in a compact format.
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?
For a tool with no output schema, the description covers critical aspects: valid statuses, response key names, pagination constraints, filter matching pitfalls, and a recovery strategy. Sufficiently complete for the tool's complexity.
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 already provides 100% parameter coverage, so baseline is 3. Description adds a concrete workflow for discovering exact filter names and warns against pluralized/partial values, which is beyond the schema. Slight extra value justifies 4.
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?
States it provides 'AWS resource availability per region' with specific statuses (isAvailableIn, isNotAvailableIn, etc.) and response keys. Explicitly distinguishes from siblings: 'Not for region counts/docs/vague queries -- use `search_documentation` / `list_regions`.'
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?
Clearly states when not to use ('region counts/docs/vague queries') and names alternatives (`search_documentation`, `list_regions`). Also gives operational prerequisites: max 10 regions, multi-region requires `filters`, single-region supports `next_token`.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieve a list of all AWS regions.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe read operation. The description adds no further behavioral context such as potential pagination, latency, or permission requirements, but the tool is simple enough that annotations may suffice.
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 a single, direct sentence with no wasted words or redundant information. It is front-loaded and easy to parse.
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?
For a tool with no parameters and no output schema, the description sufficiently explains both the action (retrieve) and the expected result (a list of all AWS regions). This is complete for the tool's simplicity.
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?
The tool has zero parameters, and the schema covers this completely. The description adds no parameter-specific detail because there are none, but the baseline score of 4 applies for tools with no parameters.
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 clearly states a specific verb and resource: 'Retrieve a list of all AWS regions.' This is distinct from sibling tools like get_regional_availability, which focuses on availability rather than a plain list of regions.
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?
The description provides no guidance on when to use this tool versus alternatives. There are no exclusions, prerequisites, or mention of sibling tools that perform related but different operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch full AWS doc pages as markdown. search_documentation already returns verbatim page chunks, so don't re-read a URL whose chunk you already have to "confirm" or "round out" an answer -- the chunk is the real page text; treat it as authoritative.
Reading the full page is justified ONLY when the chunks genuinely lack the content:
an enumeration or aggregation ("list all X", "how many X") needs the complete set and the chunks show only part of it;
no search result is on-topic after refining the query, and a known doc URL would have the answer.
Otherwise, answer from the chunks. Use exact URLs from search_documentation; don't guess slugs.
Input: requests: [{url, max_length?, start_index?}]. Batch 2-5.
max_length default 10000.
start_index default 0; use prior end_index to continue, TOC offset to jump.
Allow-listed prefixes: docs.aws.amazon.com; aws.amazon.com (not /marketplace); repost.aws/knowledge-center; docs.amplify.aws; ui.docs.amplify.aws; github.com/{aws-cloudformation/aws-cloudformation-templates, aws-samples/{aws-cdk-examples, generative-ai-cdk-constructs-samples, serverless-patterns}, awsdocs/aws-cdk-guide, awslabs/aws-solutions-constructs, cdklabs/cdk-nag} (README on main); constructs.dev/packages/{@aws-cdk-containers, @aws-cdk, @cdk-cloudformation, aws-analytics-reference-architecture, aws-cdk-lib, cdk-amazon-chime-resources, cdk-aws-lambda-powertools-layer, cdk-ecr-deployment, cdk-lambda-powertools-python-layer, cdk-serverless-clamscan, cdk8s, cdk8s-plus-33}; strandsagents.com/latest/documentation/docs/; karpenter.sh/docs/; Amazon Braket: {amazon-braket-sdk-python, amazon-braket-schemas-python, amazon-braket-default-simulator-python, amazon-braket-pennylane-plugin-python, amazon-braket-algorithm-library, qiskit-braket-provider, autoqasm, qirtoqasm}.readthedocs.io and github.com/amazon-braket/* (blob/tree/raw).
Output: SUCCESS -- markdown + total_length, start_index, end_index, truncated, redirected_url? (truncated includes TOC with char ranges). ERROR -- error_code in {not_found, invalid_url, throttled, downstream_error, validation_error}.
| Name | Required | Description | Default |
|---|---|---|---|
| requests | No | List of `{url, max_length?, start_index?}`. Batch 2-5. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true and destructiveHint=false. The description adds significant behavioral detail beyond that: it describes the output (markdown, total_length, start_index, end_index, truncated, redirected_url), error codes, and the behavior when truncated (includes TOC with char ranges). No contradictions.
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 long but well-structured: first sentence states the purpose, followed by clear usage rules, input/output details, allowed URL prefixes, and error outputs. It is front-loaded with the most critical information. The list of allowed prefixes is lengthy but necessary for correct invocation. Every sentence adds value.
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 there is no output schema, the description fully explains both success and error return formats. It covers input structure, batching, continuation mechanics, and explicit allowed URL prefixes. All aspects needed for correct invocation are addressed, leaving no critical gaps.
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% and each parameter has a schema description. The tool description adds practical usage semantics: it specifies batching 2-5, default values for max_length (10000) and start_index (0), and hints for using prior end_index to continue or TOC offset to jump. This goes beyond the schema's structural documentation.
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 clearly states 'Fetch full AWS doc pages as markdown', which is a specific verb-resource pair. It distinguishes itself from the sibling tool 'search_documentation' by explicitly noting that search returns chunks, while this tool returns the full page.
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?
Exceptionally detailed guidelines are provided: it explicitly tells the agent when NOT to use this tool (when a chunk from search already contains the needed text) and when it IS justified (enumeration/aggregation or no relevant search result). It also instructs to use exact URLs from search and not to guess slugs, which prevents misuse.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieve an AWS skill (workflows, references). Returns SKILL.md, or file if given.
Call search_documentation FIRST and copy skill_name verbatim -- it is an opaque registry ID. Never guess or fabricate skill_name or file.
| Name | Required | Description | Default |
|---|---|---|---|
| file | No | Optional file path within the skill, copied as cited (e.g. `references/architecture.md`). Don't add or strip a `references/` prefix. Omit for SKILL.md. | |
| skill_name | Yes | Required. Exact `skill_name` from a search_documentation result, copied verbatim. Do not invent or modify. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare this as read-only and non-destructive. The description adds behavioral context by explaining that `skill_name` is an 'opaque registry ID' that must be copied verbatim, and warns against fabrication. It also discloses the return behavior (SKILL.md or specific file). This goes beyond the structured annotations.
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 wasted words. It front-loads the purpose and then provides a critical usage instruction. Every sentence earns its place.
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 tool's simplicity (2 params, no output schema), the description is complete: it explains what is returned, the prerequisite step (search_documentation), and the need for exact input. The annotations cover safety aspects, so no additional behavioral disclosures are required.
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 description coverage is 100%, so the baseline is 3. The description reinforces the need to copy `skill_name` verbatim and avoid guessing, but the schema already contains these details. The description adds minimal parameter-specific meaning beyond what the schema provides, so it stays at baseline.
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 clearly states the tool's purpose: 'Retrieve an AWS skill (workflows, references). Returns SKILL.md, or `file` if given.' It specifies the resource (AWS skill) and the output behavior, and distinguishes itself from siblings by referencing the search_documentation workflow and the skill_name registry ID.
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?
The description provides explicit when-to-use guidance: 'Call `search_documentation` FIRST and copy `skill_name` verbatim.' It also warns against guessing inputs. However, it does not explicitly mention alternatives or when not to use this tool, so it stops short of a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
AWS docs search. Each result's context is verbatim page text -- a real chunk of the actual page, not a short snippet -- and usually already contains the answer, so answer directly from it. Use read_documentation only when the chunks genuinely lack the needed detail.
Pick ONE topic. Add a 2nd ONLY if query genuinely spans domains. Extra topics dilute ranking.
reference_documentation -- API/SDK/CLI specs, config params
current_awareness -- new/released/announced
troubleshooting -- errors, "how to fix" (NOT for conceptual/feature questions)
amplify_docs -- Amplify (+ language)
cdk_docs -- CDK concepts/guides
cdk_constructs -- CDK code samples, L3
cloudformation -- CFN/SAM templates
strands_docs -- Strands Agents SDK (its Skills/agents concepts go here, NOT agent_skills)
agent_skills -- this tool's guided skills (load via retrieve_skill)
general (default) -- architecture, best practices, tutorials, feature behavior
Results: rank_order (lower=better), url, title, context (verbatim page chunk -- answer directly from it).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of results to return (default 4). | |
| topics | No | Up to 3 from: reference_documentation, current_awareness, troubleshooting, amplify_docs, cdk_docs, cdk_constructs, cloudformation, agent_skills, strands_docs, general. Default ["general"]. | |
| search_phrase | Yes | Keywords; preserve exact error strings and all task terms verbatim. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint=true, destructiveHint=false), the description discloses key behavioral traits: each result's context is 'verbatim page text' that 'usually already contains the answer', and that 'Extra topics dilute ranking.' These are non-obvious behavioral details that help the agent interpret results correctly, with no contradiction with annotations.
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 front-loaded with purpose and key behavior, then uses a well-organized bullet list for topics. Every sentence earns its place: the topic list is necessary, and the result format note is essential in the absence of an output schema. No fluff or redundancy.
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 3 parameters, no output schema, and moderate complexity, the description provides full context: it explains result fields (rank_order, url, title, context), the nature of context (verbatim chunks), and how to bridge to read_documentation. It covers all needed operational knowledge for an agent to select and invoke the tool correctly.
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?
Although the schema has 100% parameter descriptions, the description adds significant meaning by defining each topic category (e.g., 'troubleshooting -- errors, "how to fix" (NOT for conceptual/feature questions)') and explaining topic selection strategy. This provides semantic enrichment beyond the schema's terse 'topic name' descriptions.
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 begins with 'AWS docs search', clearly stating the tool's verb (search) and resource (AWS docs). It distinguishes itself from siblings by explicitly directing users to 'read_documentation' only when chunks lack detail, and references 'retrieve_skill' in the topic list. This is a specific, differentiated purpose.
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?
Provides explicit when-to-use guidance: 'Use read_documentation only when the chunks genuinely lack the needed detail,' and instructs 'Pick ONE topic. Add a 2nd ONLY if query genuinely spans domains.' It also clarifies that troubleshooting is NOT for conceptual/feature questions. This fully covers usage context and alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claiming 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 or an account that owns the GitHub organization, then choose Claim with GitHub.
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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The Remote MCP server acts as a standardized bridge between LLM applications (like Claude, ChatGPT, and Cursor) and external services, enabling AI agents to access external tools and resources. Its primary capability is providing a centralized search tool to discover other MCP servers and their respective tools. Unlike local implementations, it runs remotely with OAuth authentication and permission controls for security.
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Each tool targets a distinct aspect of AWS knowledge: regional availability, region listing, documentation search, full page retrieval, and skill retrieval. Descriptions clearly differentiate when to use each, especially distinguishing search from read_documentation.
All tools follow a consistent verb_noun pattern with snake_case, prefixed by 'aws___.' Names like get_regional_availability, list_regions, read_documentation, retrieve_skill, and search_documentation are descriptive and predictable.
Five tools is well-scoped for a knowledge server. Each tool provides a core function (search, full doc retrieval, region listing, availability check, skills) without redundancy or excessive specialization.
The tool set covers primary AWS knowledge needs: searching documentation, reading full pages, listing regions, checking regional availability, and retrieving skills. Minor gap: no direct tool for enumerating all services, but search_documentation can handle that.
